D.A 2D array of discrete picture elements called pixels
Correct Answer: A 2D array of discrete picture elements called pixels
Explanation:
A digital image can be defined as a two-dimensional function, , where and are spatial coordinates, and the amplitude at any given pair of coordinates is called the intensity or gray level of the image at that point, which corresponds to a pixel.
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2What is the primary goal of digital image processing?
Introduction to image processing
Easy
A.To reduce the physical size of a computer monitor
B.To convert 2D images directly into audio files
C.To generate 3D models from scratch without any input data
D.To improve image data for human interpretation or machine perception
Correct Answer: To improve image data for human interpretation or machine perception
Explanation:
Image processing involves manipulating digital images to enhance them for human viewers or to extract useful information for machine processing and interpretation.
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3In terms of inputs and outputs, how does Image Processing generally differ from Computer Vision?
key differences between image processing and computer vision
Easy
A.Image processing outputs an image, while computer vision outputs understanding or decisions.
B.Image processing outputs text, while computer vision outputs images.
C.There is no difference; the terms mean exactly the same thing.
D.Image processing only deals with 3D models, while computer vision deals with 2D images.
Correct Answer: Image processing outputs an image, while computer vision outputs understanding or decisions.
Explanation:
Image processing typically takes an image as input and returns a modified image as output. Computer vision takes an image as input and outputs a high-level understanding, such as object labels or measurements.
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4Which of the following tasks is considered a Computer Vision task rather than basic Image Processing?
key differences between image processing and computer vision
Easy
A.Object detection and facial recognition
B.Adjusting image brightness and contrast
C.Sharpening a blurry photograph
D.Converting a color image to grayscale
Correct Answer: Object detection and facial recognition
Explanation:
Tasks like sharpening and adjusting brightness are image processing techniques (image in, image out). Object detection requires the computer to 'understand' the content of the image, making it a computer vision task.
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5Which of the following is a common medical application of image processing?
applications of image processing and computer vision
Easy
A.Enhancing X-ray, CT, and MRI scans for better diagnosis
B.Creating financial spreadsheets for hospital administration
C.Sending text messages between doctors
D.Compiling software code for hospital databases
Correct Answer: Enhancing X-ray, CT, and MRI scans for better diagnosis
Explanation:
Medical imaging heavily relies on image processing techniques to enhance the visual quality of scans (like X-rays and MRIs) to help doctors detect diseases and abnormalities more accurately.
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6Automated face recognition used to unlock modern smartphones is an application of which field?
applications of image processing and computer vision
Easy
A.Database Management
B.Audio Signal Processing
C.Natural Language Processing
D.Computer Vision
Correct Answer: Computer Vision
Explanation:
Face recognition requires the system to analyze an image, detect facial features, and verify identity. This high-level interpretation falls under the domain of Computer Vision.
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7Which standard image file format is widely known for utilizing lossy compression to reduce file size?
image file formats
Easy
A.PNG
B.TIFF
C.JPEG
D.BMP
Correct Answer: JPEG
Explanation:
JPEG (Joint Photographic Experts Group) uses lossy compression, which permanently removes some image data to significantly reduce the file size, making it ideal for web use.
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8What does the acronym PNG stand for in the context of image file formats?
image file formats
Easy
A.Picture Network Group
B.Portable Network Graphics
C.Pixel Node Graphics
D.Public Network Grid
Correct Answer: Portable Network Graphics
Explanation:
PNG stands for Portable Network Graphics. It is a widely used lossless image format that supports transparent backgrounds.
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9What does 'contrast' refer to in a digital image?
contrast enhancement
Easy
A.The specific file extension (e.g., .jpg vs .png) of the image
B.The amount of disk space the image occupies
C.The physical size of the printed image
D.The difference in luminance or color that makes an object distinguishable from its background
Correct Answer: The difference in luminance or color that makes an object distinguishable from its background
Explanation:
Contrast is the visual difference between the brightest and darkest parts of an image. Higher contrast means greater differences in intensity levels.
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10Which technique is primarily used to make a dark, low-visibility image appear clearer by expanding its range of pixel intensities?
contrast enhancement
Easy
A.Image compression
B.Contrast enhancement
C.Image cropping
D.Format conversion
Correct Answer: Contrast enhancement
Explanation:
Contrast enhancement adjusts the intensity values of an image to increase the visual distinction between different areas, making dark or washed-out images clearer.
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11What does an image histogram graphically represent?
histogram equalization and specification
Easy
A.The physical dimensions of the image in pixels
B.The frequency distribution of pixel intensity values across the image
C.The number of unique objects detected within the image
D.The size of the image file in kilobytes
Correct Answer: The frequency distribution of pixel intensity values across the image
Explanation:
A histogram plots the number of pixels for each intensity value (e.g., 0 to 255 in an 8-bit grayscale image), showing the overall distribution of tones in the image.
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12What is the main objective of histogram equalization?
histogram equalization and specification
Easy
A.To achieve a more uniform distribution of pixel intensities to enhance overall contrast
B.To reduce the image resolution and save storage space
C.To add artificial colors to a grayscale image
D.To change the image file format from JPEG to PNG
Correct Answer: To achieve a more uniform distribution of pixel intensities to enhance overall contrast
Explanation:
Histogram equalization transforms the image so that its histogram is stretched and flattened, which globally enhances the contrast of the image.
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13What characterizes 'Salt and Pepper' noise in a digital image?
image noise and its types
Easy
A.A type of blur caused by camera motion
B.A digital watermark superimposed on the image
C.Color distortion caused by poor lighting conditions
D.Random occurrences of pure black and pure white pixels
Correct Answer: Random occurrences of pure black and pure white pixels
Explanation:
Salt and pepper noise, also known as impulse noise, appears as stark white ('salt') and black ('pepper') pixels randomly scattered across the image.
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14Which type of image noise is often modeled mathematically using a bell-shaped (normal) distribution?
image noise and its types
Easy
A.Quantization noise
B.Impulse noise
C.Gaussian noise
D.Speckle noise
Correct Answer: Gaussian noise
Explanation:
Gaussian noise is statistical noise having a probability density function equal to that of the normal distribution, also known as the Gaussian distribution.
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15What typically causes noise in digital photographs taken in low light?
image noise and its types
Easy
A.Saving the image as a lossless PNG file
B.Using a high-resolution 4K monitor
C.Taking a photograph of a perfectly smooth surface
D.High sensor sensitivity (ISO) and electronic interference
Correct Answer: High sensor sensitivity (ISO) and electronic interference
Explanation:
In low light, digital cameras boost sensor sensitivity (ISO), which amplifies both the light signal and the inherent electronic background interference, resulting in visible noise.
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16What does a spatial domain filter operate directly on?
spatial domain filtering techniques
Easy
A.The individual pixels of the image
B.The color metadata of the image file
C.The geometric coordinates of detected objects
D.The Fourier transform coefficients of the image
Correct Answer: The individual pixels of the image
Explanation:
Spatial domain filtering involves manipulating the image directly by calculating new pixel values based on the original pixels and their immediate neighbors.
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17Which spatial filter is highly effective at removing salt-and-pepper noise while preserving sharp edges?
spatial domain filtering techniques
Easy
A.Median filter
B.High-pass filter
C.Laplacian filter
D.Mean (Average) filter
Correct Answer: Median filter
Explanation:
A median filter replaces each pixel with the median value of its neighboring pixels. This easily ignores extreme black and white values (salt and pepper) without blurring edges as much as a mean filter.
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18A spatial smoothing (low-pass) filter is primarily used for what purpose?
spatial domain filtering techniques
Easy
A.Detecting sharp edges in the image
B.Blurring the image and reducing noise
C.Compressing the image file size
D.Increasing the global image contrast
Correct Answer: Blurring the image and reducing noise
Explanation:
Smoothing filters average out rapid changes in pixel intensities. This has the effect of blurring the image and reducing high-frequency noise.
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19Which mathematical transform is used to convert an image from the spatial domain into the frequency domain?
frequency domain filtering
Easy
A.Affine Transform
B.Hough Transform
C.Distance Transform
D.Fourier Transform
Correct Answer: Fourier Transform
Explanation:
The Fourier Transform decomposes an image into its sine and cosine components, allowing operations to be performed on the frequency domain representation of the image.
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20In the frequency domain, what do high frequencies generally correspond to in an image?
frequency domain filtering
Easy
A.The physical dimensions (width and height) of the image
B.Smooth backgrounds and uniform color areas
C.The overall average brightness of the image
D.Edges, fine details, and sharp transitions in intensity
Correct Answer: Edges, fine details, and sharp transitions in intensity
Explanation:
High frequencies represent rapid changes in pixel intensity over a short spatial distance, which visually correspond to edges, noise, and fine details.
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21Which of the following scenarios best illustrates the transition from Image Processing to Computer Vision in an automated pipeline?
key differences between image processing and computer vision
Medium
A.Enhancing the contrast of a medical X-ray, followed by an algorithm that identifies and classifies a tumor.
B.Applying a median filter to remove noise, followed by using a Sobel operator to detect edges.
C.Converting an RGB image to grayscale, and then applying histogram equalization to improve contrast.
D.Compressing a high-resolution image into a JPEG format, followed by transmitting it over a network.
Correct Answer: Enhancing the contrast of a medical X-ray, followed by an algorithm that identifies and classifies a tumor.
Explanation:
Image processing involves manipulating an image to produce another image (e.g., contrast enhancement). Computer vision takes an image as input and produces high-level semantic information or decisions as output (e.g., classifying a tumor).
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22In a digital camera's software, which sequence correctly identifies the roles of Image Processing (IP) and Computer Vision (CV)?
key differences between image processing and computer vision
Medium
A.IP: Estimating the depth of the scene; CV: Adjusting the brightness.
B.IP: Compressing the image to JPEG; CV: Sharpening the image.
C.IP: Applying white balance and noise reduction; CV: Detecting faces to draw bounding boxes.
D.IP: Recognizing a face for autofocus; CV: Applying white balance.
Correct Answer: IP: Applying white balance and noise reduction; CV: Detecting faces to draw bounding boxes.
Explanation:
White balance and noise reduction output an improved image, which falls under Image Processing. Detecting a face to extract features or draw a bounding box interprets the image content, falling under Computer Vision.
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23If a continuous image is digitized such that the spatial resolution is increased while the amplitude resolution (quantization levels) is decreased, what is the most likely visual effect on the resulting digital image?
Introduction to image processing
Medium
A.The image will have sharper details but will exhibit false contouring.
B.The image will become blurry and exhibit a checkerboard pattern.
C.The image will appear exactly the same as long as the file size remains constant.
D.The image will have smoother color transitions but lose high-frequency edge details.
Correct Answer: The image will have sharper details but will exhibit false contouring.
Explanation:
Increasing spatial resolution improves the capture of fine details (sharper edges). However, decreasing amplitude resolution (fewer bits per pixel) reduces the number of available colors/intensities, leading to noticeable sudden jumps in gradients, known as false contouring.
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24In the context of Optical Character Recognition (OCR), which of the following represents a Computer Vision task rather than a preliminary Image Processing task?
applications of image processing and computer vision
Medium
A.Applying a morphological closing operation to bridge gaps in broken text strokes.
B.Correcting the skew of the scanned document.
C.Mapping a segmented connected component to the letter 'A' using a trained model.
D.Binarizing the scanned document using Otsu's thresholding.
Correct Answer: Mapping a segmented connected component to the letter 'A' using a trained model.
Explanation:
Thresholding, skew correction, and morphological operations output modified images to improve readability (Image Processing). Recognizing and classifying a shape as a specific semantic character ('A') is Computer Vision.
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25Which of the following applications relies primarily on Image Processing techniques without necessarily requiring Computer Vision?
applications of image processing and computer vision
Medium
A.Automated reading of vehicle license plates at a toll booth.
B.Facial recognition for smartphone unlocking.
C.Autonomous driving obstacle avoidance.
D.Restoration of an old, degraded photograph by removing scratches.
Correct Answer: Restoration of an old, degraded photograph by removing scratches.
Explanation:
Restoring an image takes an input image and outputs an enhanced/repaired image, which is pure Image Processing. The other options require interpreting the image content to make decisions or extract data (Computer Vision).
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26A researcher is storing images containing sharp text and line drawings for an archival dataset. They need a format that avoids artifacts around the text edges and supports a lossless workflow. Which of the following formats is the most appropriate?
image file formats
Medium
A.PNG
B.Lossy WebP
C.GIF
D.JPEG
Correct Answer: PNG
Explanation:
PNG uses lossless compression and does not introduce ringing or block artifacts, making it ideal for sharp text and line drawings. JPEG uses lossy discrete cosine transform compression, which typically introduces visible artifacts around sharp, high-contrast edges.
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27Which characteristic makes the TIFF file format highly suitable for professional medical and satellite imaging?
image file formats
Medium
A.It is an animation-only format that allows for the viewing of 3D medical scans.
B.It is highly compressed, resulting in the smallest file sizes for web transmission.
C.It inherently restricts the color depth to 8-bit, standardizing image analysis.
D.It supports high bit-depths, lossless compression, and can store multiple images (pages) in a single file.
Correct Answer: It supports high bit-depths, lossless compression, and can store multiple images (pages) in a single file.
Explanation:
TIFF is highly flexible, supporting lossless compression (like LZW), high bit-depths (e.g., 16-bit or 32-bit images needed for medical and satellite data), and multiple layers/pages.
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28An image captured in a low-light environment appears very dark. To enhance the image using a power-law (Gamma) transformation , what should be the value of ?
contrast enhancement
Medium
A.
B.
C.
D.
Correct Answer:
Explanation:
A fractional gamma value () maps a narrow range of dark input values into a wider range of output values, effectively brightening the image and revealing details in the shadows.
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29What is the primary purpose of applying a Log transformation to an image?
contrast enhancement
Medium
A.To threshold the image into a binary format.
B.To increase the contrast of the bright pixels while compressing the dark pixels.
C.To invert the image intensities, producing a negative image.
D.To compress the dynamic range of an image, expanding dark pixels and compressing bright pixels.
Correct Answer: To compress the dynamic range of an image, expanding dark pixels and compressing bright pixels.
Explanation:
Log transformations are used to compress the dynamic range. They expand the values of dark pixels while compressing higher-level values. This is heavily used to display the Fourier spectrum of an image.
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30In piecewise linear transformation, what is the effect of setting the coordinates and such that and , (where is the number of intensity levels)?
contrast enhancement
Medium
A.It performs a thresholding operation, creating a binary image.
B.It applies contrast stretching.
C.It reverses the image, creating a negative.
D.It performs intensity-level slicing.
Correct Answer: It performs a thresholding operation, creating a binary image.
Explanation:
When , the transformation function becomes a vertical line at that value. Any input below maps to 0, and any input above maps to , which is the definition of a strict thresholding operation.
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31If global histogram equalization is applied to an image whose histogram is heavily clustered at the lower intensity levels (dark image), what will be the characteristic of the resulting output image's histogram?
histogram equalization and specification
Medium
A.It will become completely flat with exactly equal numbers of pixels at every intensity level.
B.It will be stretched across the entire intensity range, though not perfectly flat due to discrete pixel values.
C.It will be shifted perfectly to the higher intensity levels, making the image overly bright.
D.It will remain clustered at the lower intensity levels, but the contrast will decrease.
Correct Answer: It will be stretched across the entire intensity range, though not perfectly flat due to discrete pixel values.
Explanation:
Histogram equalization aims to distribute intensities uniformly. For discrete digital images, true flatness is rarely achieved because pixels with the same input intensity cannot be mapped to different output intensities, but the overall range is stretched.
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32Under what circumstance is Histogram Specification (Matching) preferred over standard Histogram Equalization?
histogram equalization and specification
Medium
A.When the image requires maximum global contrast enhancement without any specific target distribution.
B.When we need the output image to have a specific predefined histogram shape rather than a uniform one.
C.When processing time is strictly limited, as specification is computationally faster than equalization.
D.When the image contains salt-and-pepper noise that needs to be removed.
Correct Answer: When we need the output image to have a specific predefined histogram shape rather than a uniform one.
Explanation:
Histogram equalization forces a uniform distribution, which might wash out certain features. Histogram specification allows the user to define a specific target histogram, offering controlled enhancement suited for a specific application.
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33Let be the probability density function of the input image and be the transformation function for histogram equalization. What mathematical property must satisfy to ensure the output image intensities retain their logical order without inversion?
histogram equalization and specification
Medium
A. must be a periodic function.
B. must be a step function.
C. must be a strictly decreasing function.
D. must be a monotonically increasing function.
Correct Answer: must be a monotonically increasing function.
Explanation:
To preserve the order from dark to bright (meaning if pixel A is brighter than pixel B originally, it stays brighter or equal after transformation) and prevent artifacts, must be monotonically increasing.
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34Which type of noise is characterized by a probability density function (PDF) that consists of two distinct impulses at the extremes of the intensity range?
image noise and its types
Medium
A.Speckle noise
B.Gaussian noise
C.Uniform noise
D.Salt-and-pepper (impulse) noise
Correct Answer: Salt-and-pepper (impulse) noise
Explanation:
Salt-and-pepper noise appears as random black and white pixels. Its PDF consists of impulses at the minimum (pepper/0) and maximum (salt/255) intensity values.
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35In medical ultrasound imaging, images are often degraded by a granular noise pattern. This noise is multiplicative in nature. What type of noise is this?
image noise and its types
Medium
A.Gaussian noise
B.Periodic noise
C.Speckle noise
D.Salt-and-pepper noise
Correct Answer: Speckle noise
Explanation:
Speckle noise is a multiplicative noise commonly found in coherent imaging systems like ultrasound and synthetic aperture radar (SAR). It creates a granular pattern that degrades image quality.
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36Why is a median filter generally preferred over an average (mean) filter for removing salt-and-pepper noise?
spatial domain filtering techniques
Medium
A.Because the average filter increases the dynamic range of the noise.
B.Because the average filter severely blurs the edges, whereas the median filter preserves edge sharpness while replacing the extreme noise values.
C.Because the median filter is a linear filter and therefore computationally faster.
D.Because the median filter alters all pixel values in the image uniformly.
Correct Answer: Because the average filter severely blurs the edges, whereas the median filter preserves edge sharpness while replacing the extreme noise values.
Explanation:
The median filter is a non-linear filter that replaces a pixel with the median of its neighborhood. Extreme values (noise) are naturally ignored in a sorted list, removing the noise effectively without the significant blurring of edges caused by averaging.
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37Which of the following spatial filters would you apply to highlight fine details and enhance blurred edges in an image?
spatial domain filtering techniques
Medium
A.A median filter.
B.A matrix filled entirely with .
C.A Laplacian filter.
D.A Gaussian smoothing filter.
Correct Answer: A Laplacian filter.
Explanation:
The Laplacian filter is a second-order derivative filter used to detect regions of rapid intensity change (edges). It is commonly used for image sharpening to highlight fine details.
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38In unsharp masking, the sharpened image is obtained using the formula: . How is the generated?
spatial domain filtering techniques
Medium
A.By taking the first derivative of the original image.
B.By subtracting a blurred (low-pass filtered) version of the image from the original image.
C.By adding a Gaussian noise pattern to the original image.
D.By applying a high-pass filter directly to the original image.
Correct Answer: By subtracting a blurred (low-pass filtered) version of the image from the original image.
Explanation:
Unsharp masking involves creating a 'mask' of the high-frequency components by subtracting a blurred version of the image from the original. This mask is then scaled and added back to the original image.
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39When applying an Ideal Lowpass Filter (ILPF) in the frequency domain, what visible artifact is most commonly introduced in the spatial domain output?
frequency domain filtering
Medium
A.False contouring
B.Multiplicative speckle
C.Salt-and-pepper noise
D.Ringing effect (Gibbs phenomenon)
Correct Answer: Ringing effect (Gibbs phenomenon)
Explanation:
An Ideal Lowpass Filter has a sharp, sudden cutoff in the frequency domain. Due to the properties of the Fourier transform, this sharp rectangle translates to a sinc function in the spatial domain, causing a visible 'ringing' artifact around edges.
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40In the frequency domain, what structural components of an image generally map to the high-frequency regions of the Fourier spectrum?
frequency domain filtering
Medium
A.Smooth gradients and uniform backgrounds.
B.Slowly varying color transitions.
C.The average brightness (DC component) of the image.
D.Edges, sharp transitions, and noise.
Correct Answer: Edges, sharp transitions, and noise.
Explanation:
High frequencies in the Fourier domain correspond to rapid changes in pixel intensities in the spatial domain. These rapid changes represent edges, fine details, and random noise.
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41Which of the following fundamentally distinguishes the architectural endpoint of a Computer Vision (CV) system from a classical Digital Image Processing (DIP) system in the context of the semantic gap?
Key differences between image processing and computer vision
Hard
A.CV systems map multi-dimensional arrays to 1D vectors, whereas DIP systems always maintain 3D topological outputs.
B.DIP minimizes the semantic gap by strictly using lossless compression, while CV uses lossy compression.
C.DIP systems output an enhanced scalar matrix, whereas CV systems output highly abstracted semantic descriptors or decisions.
D.DIP systems utilize spatial domain operations exclusively, whereas CV systems rely entirely on frequency domain transforms.
Correct Answer: DIP systems output an enhanced scalar matrix, whereas CV systems output highly abstracted semantic descriptors or decisions.
Explanation:
Classical Image Processing takes an image as input and outputs a modified image (matrix), focusing on enhancement or compression. Computer Vision aims to bridge the semantic gap by extracting meaning, outputting features, classifications, or semantic decisions rather than just pixel arrays.
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42Given a continuous image with intensity values in the range and probability density function , histogram equalization applies the transformation . By applying the fundamental theorem of calculus and probability transformation principles, what is the resulting probability density function of the output image?
Histogram equalization and specification
Hard
A. for
B.
C. for
D.
Correct Answer: for
Explanation:
By probability theory, . Since , its derivative is . Substituting this yields , which is a uniform distribution.
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43Why does discrete histogram equalization rarely result in a perfectly flat (uniform) histogram in practice, unlike the continuous formulation?
Histogram equalization and specification
Hard
A.Because rounding errors in the discrete integral cause high-frequency attenuation.
B.Because discrete transformations cannot break pixel groupings; all pixels with a specific input intensity must map to exactly the same output intensity .
C.Because spatial filtering is required before discrete equalization can map non-linear intensities.
D.Because the number of output bins is always strictly less than the number of input bins.
Correct Answer: Because discrete transformations cannot break pixel groupings; all pixels with a specific input intensity must map to exactly the same output intensity .
Explanation:
Histogram equalization is a point operation. It cannot differentiate between pixels that have the same initial intensity value; they all map to the same new value. Therefore, it cannot split a populated bin to perfectly flatten the discrete histogram.
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44Consider a power-law (Gamma) transformation applied to an image with intensity range . If a nested transformation is applied such that followed by , which condition must hold for the final image to strictly compress the dynamic range of the dark regions while expanding the dynamic range of the bright regions?
Contrast enhancement
Hard
A.
B.
C.
D.
Correct Answer:
Explanation:
The composite transformation is . To compress dark regions (making them darker and narrower in range) and expand bright regions, the overall gamma must be greater than 1.
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45An image is degraded by noise whose probability density function is given by for , and $0$ otherwise. This noise model is particularly useful for characterizing noise in which of the following imaging modalities?
Correct Answer: Magnetic Resonance Imaging (MRI) and range imaging, representing Rayleigh noise.
Explanation:
The given PDF describes Rayleigh noise. Rayleigh noise distributions are commonly used to model the statistical behavior of noise in MRI images, range imaging, and ultrasound.
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46An alpha-trimmed mean filter is applied to an neighborhood. It operates by sorting the pixels, deleting the lowest and highest values, and averaging the rest. If , what does this filter mathematically reduce to?
Spatial domain filtering techniques
Hard
A.A Max filter
B.A Median filter
C.A Min filter
D.An Arithmetic Mean filter
Correct Answer: A Median filter
Explanation:
If , the filter removes all pixels except for the single middle pixel in the sorted array (since is typically odd for a symmetric neighborhood). This single remaining value is the median of the neighborhood.
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47The Unsharp Masking technique for image sharpening is defined as , where is a blurred version of . If the weighting constant , this process is specifically known as:
Spatial domain filtering techniques
Hard
A.Laplacian of Gaussian filtering
B.Sobel gradient extraction
C.High-boost filtering
D.Homomorphic filtering
Correct Answer: High-boost filtering
Explanation:
When , the process is standard unsharp masking. When , the contribution of the high-frequency components (the edge mask) is amplified relative to the original image, a technique known as high-boost filtering.
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48An Ideal Lowpass Filter (ILPF) in the frequency domain is a radial cylinder function. Why does applying an ILPF severely introduce 'ringing' artifacts in the spatial domain?
Frequency domain filtering
Hard
A.Because the ILPF introduces non-linear phase shifts at the cutoff frequency.
B.Because removing high frequencies violates the Nyquist-Shannon sampling theorem, causing aliasing that manifests as rings.
C.Because the inverse Fourier transform of a 2D cylinder is a 2D sinc function (or Bessel function of the first kind), which has infinite oscillating side lobes.
D.Because the sharp cutoff acts as a spatial domain high-pass filter, accentuating edges periodically.
Correct Answer: Because the inverse Fourier transform of a 2D cylinder is a 2D sinc function (or Bessel function of the first kind), which has infinite oscillating side lobes.
Explanation:
Filtering in the frequency domain equates to convolution in the spatial domain. The inverse Fourier transform of an ideal circular step function (cylinder) is a continuous oscillating function (jinc or 2D sinc). Convolving an image with these oscillations causes the visible ringing effect.
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49Consider a Butterworth Lowpass Filter (BLPF) of order , defined as . As the order , which phenomenon becomes most prominent when filtering a natural image?
Frequency domain filtering
Hard
A.The filter perfectly preserves both high and low frequencies, eliminating any blurring.
B.The BLPF approaches an Ideal Lowpass Filter, resulting in pronounced spatial ringing artifacts.
C.The BLPF approaches a Gaussian Lowpass Filter, ensuring zero ringing artifacts.
D.The transition band of the filter becomes perfectly flat, rendering the filter equivalent to an identity transform.
Correct Answer: The BLPF approaches an Ideal Lowpass Filter, resulting in pronounced spatial ringing artifacts.
Explanation:
As the order increases, the transition between the passband and the stopband of the Butterworth filter becomes infinitely steep. At the limit , it behaves exactly like an Ideal Lowpass Filter, which is notorious for causing spatial ringing.
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50Homomorphic filtering is designed to improve the appearance of an image by simultaneous intensity range compression and contrast enhancement. It achieves this by modeling the image as the product of illumination and reflectance . What is the exact sequence of operations in homomorphic filtering?
Homomorphic filtering applies a natural log to separate the multiplicative components (illumination and reflectance) into additive ones. Then, the Fourier transform is applied. A frequency domain filter (usually high-frequency emphasis to reduce low-freq illumination and boost high-freq reflectance) is applied, followed by the inverse Fourier transform, and finally, an exponential function to invert the initial logarithm.
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51In the baseline JPEG compression standard, the spatial domain image is divided into blocks and transformed using the Discrete Cosine Transform (DCT). Which step in the JPEG pipeline is primarily responsible for its lossy nature and achieves the most significant compression?
Image file formats
Hard
A.Huffman or Arithmetic entropy coding of the zig-zag scanned AC coefficients.
B.Differential Pulse Code Modulation (DPCM) applied to the DC coefficients.
C.The application of the forward Discrete Cosine Transform to the spatial blocks.
D.Element-wise division of the DCT coefficients by a quantization matrix followed by rounding.
Correct Answer: Element-wise division of the DCT coefficients by a quantization matrix followed by rounding.
Explanation:
The DCT itself is technically lossless (ignoring floating-point precision). The actual loss of information—and the bulk of compression—occurs during quantization, where high-frequency coefficients are divided by large numbers and rounded to zero.
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52Salt-and-pepper noise is a bipolar impulse noise where pixels are randomly degraded to the maximum (salt) or minimum (pepper) intensity values. If the probability of salt is and pepper is , what is the theoretical spatial domain limit of a purely linear spatial filter (like a mean filter) in removing this noise at high noise densities ()?
Image noise and its types
Hard
A.Linear filters convert bipolar impulse noise into Gaussian noise, which can then be easily removed using a secondary linear filter.
B.Linear filters are mathematically proven to perfectly eliminate impulse noise if the kernel size strictly equals .
C.Linear filters fail because they distribute the extreme outlier values across the neighborhood, causing pervasive blurring without eliminating the impulses.
D.Linear filters will successfully remove the noise but cause aliasing in the frequency domain.
Correct Answer: Linear filters fail because they distribute the extreme outlier values across the neighborhood, causing pervasive blurring without eliminating the impulses.
Explanation:
Linear filters (like the mean filter) compute weighted averages. When they encounter extreme values (impulses), they do not discard them; they incorporate them into the average. This spreads the noise, resulting in a blurred image where the impulses are smeared rather than removed.
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53The continuous Laplacian is a linear, isotropic second-derivative operator defined as . When discretizing this operator for digital image processing using a kernel that incorporates diagonal neighbors, what must the sum of all coefficients in the mask equal, and why?
Spatial domain filtering techniques
Hard
A.One, to preserve the average background intensity of the image during convolution.
B.-1, to ensure the high-frequency components are strictly inverted before edge masking.
C.Zero, because the derivative of a constant intensity area must yield zero.
D.Eight, because there are exactly 8 neighbors contributing to the central pixel.
Correct Answer: Zero, because the derivative of a constant intensity area must yield zero.
Explanation:
Derivative operators measure the rate of change. In areas of constant intensity (no change), the output must be zero. Therefore, the sum of the coefficients in any linear derivative spatial mask (including the Laplacian) must mathematically sum to zero.
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54According to the 2D Nyquist-Shannon sampling theorem, undersampling a continuous image containing high spatial frequencies leads to aliasing. In digital images, aliasing frequently manifests as visually distinct, low-frequency artifact patterns that do not exist in the original scene. What is the technical term for these patterns?
Introduction to image processing
Hard
A.Moiré patterns
B.Mach bands
C.Gibbs phenomenon
D.Speckle artifacts
Correct Answer: Moiré patterns
Explanation:
Moiré patterns occur when a high-frequency repeating pattern is undersampled, causing the high frequencies to 'fold back' into the low-frequency spectrum, creating a false, low-frequency wave pattern.
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55Bit-plane slicing breaks down an 8-bit image into 8 binary planes. Plane 7 contains the Most Significant Bits (MSB) and Plane 0 contains the Least Significant Bits (LSB). If a complex natural image is subjected to bit-plane slicing, what is the typical visual characteristic of Plane 0?
Contrast enhancement
Hard
A.It contains the global illumination gradient, appearing as a smooth transition from dark to light.
B.It perfectly retains the sharpest structural edges of the objects but lacks any gray-level shading.
C.It resembles a high-contrast binary silhouette of the primary objects in the image.
D.It appears as visually unstructured, random noise because it represents the finest, lowest-magnitude intensity variations.
Correct Answer: It appears as visually unstructured, random noise because it represents the finest, lowest-magnitude intensity variations.
Explanation:
The least significant bit (Plane 0) changes extremely rapidly with minor signal variations and noise. Thus, it contains the finest details which visually manifest as random noise, holding almost no structural geometric information of the scene.
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56In an Automated Optical Inspection (AOI) pipeline for printed circuit boards (PCBs), a system must identify microscopic hairline fractures in copper traces while ignoring uniform, large variations in solder mask illumination. Which sequence of operations represents the most robust algorithmic synthesis for this task?
Applications of image processing and computer vision
Hard
A.Apply a Gaussian Lowpass Filter followed by a morphological erosion with a large structuring element.
B.Apply a Top-Hat morphological transform followed by Canny edge detection.
C.Apply global histogram equalization followed by an Ideal Lowpass Filter.
D.Apply an arithmetic mean filter followed by global thresholding.
Correct Answer: Apply a Top-Hat morphological transform followed by Canny edge detection.
Explanation:
The Top-Hat transform (image minus its morphological opening) is excellent for extracting small elements and details (like hairline fractures) from a nonuniform background (illumination variations). Following this with a Canny edge detector precisely isolates the fractures.
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57To properly display or design filters for the 2D Discrete Fourier Transform (DFT) of an image , it is common practice to shift the zero-frequency (DC) component to the center of the frequency grid. Mathematically, how is this achieved strictly in the spatial domain prior to the transform?
Frequency domain filtering
Hard
A.By convolving with a Dirac delta function
B.By multiplying by
C.By multiplying by
D.By taking the absolute value of and dividing by its maximum
Correct Answer: By multiplying by
Explanation:
According to the translation properties of the Fourier Transform, multiplying the spatial domain image by (which is equivalent to ) shifts the origin of the DFT to , centering the DC component.
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58The Tagged Image File Format (TIFF) relies on a specific structural hierarchy to support immense flexibility, allowing it to contain multiple images, varying color spaces, and different compression algorithms (LZW, ZIP, etc.) in one file. What is the core data structure inside a TIFF that enables this flexibility?
Image file formats
Hard
A.Image File Directories (IFDs) containing arrays of tagged entries.
B.A globally linked Huffman tree defining the color palette.
TIFF files are structured using Image File Directories (IFDs). Each IFD contains a list of 'tags' (hence the name). Tags define metadata, pointers to the image data, color space, and compression, allowing multiple highly customized images to exist sequentially in a single file via an IFD linked list.
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59The Sobel edge detection operator utilizes a kernel to approximate the gradient of the image. The -direction kernel uses values . What is the theoretical justification for using a weight of '2' for the center row, as opposed to a uniform weight of '1' used in the Prewitt operator?
Spatial domain filtering techniques
Hard
A.It ensures that the convolution sum reaches exactly zero, which Prewitt kernels fail to achieve.
B.It strictly satisfies the Nyquist criterion for discrete spatial derivatives, preventing edge aliasing.
C.It provides greater smoothing along the axis orthogonal to the derivative, reducing the effect of high-frequency noise on edge detection.
D.It is required to make the kernel separable into 1D filters, which is mathematically impossible for the Prewitt operator.
Correct Answer: It provides greater smoothing along the axis orthogonal to the derivative, reducing the effect of high-frequency noise on edge detection.
Explanation:
The Sobel operator is technically a combination of a derivative operation in one direction and a smoothing (weighted averaging) operation in the orthogonal direction. The weight of '2' in the center gives more importance to the center pixel, approximating a Gaussian smoothing function better than the uniform Prewitt mask.
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60A continuous image undergoes a contrast stretching transformation defined by a piecewise linear function. If the function maps the intensity range to , and it is strictly true that while and , what does this specific transformation behave as?
Contrast enhancement
Hard
A.A hard binary thresholding function at threshold .
B.A purely linear negation (image inversion) function.
C.A histogram equalization function matching a uniform distribution.
D.An identity transformation leaving the image unchanged.
Correct Answer: A hard binary thresholding function at threshold .
Explanation:
When the points and vertically align at , the slope of the mapping function in that range becomes infinite. Any input pixel intensity is mapped to $0$, and any is mapped to , which precisely defines global binary thresholding.