MCQs

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Overall MCQs for Reference

Which of the following is a basic intensity transformation function used in spatial domain image enhancement?
a. Gaussian filter
b. Laplacian filter
c. Power-law transformation
d. Butterworth filter
Answer: c. Power-law transformationscribd+1
What type of spatial filter is commonly used to reduce image noise and blur?
a. High pass filter
b. Sharpening filter
c. Low pass filter
d. Unsharp masking filter
Answer: c. Low pass filtereasyexamnotes+1
Which technique combines multiple enhancement methods to improve image quality?
a. Histogram equalization
b. Median filtering
c. Adaptive filtering
d. Image fusion
Answer: d. Image fusionscribd+1
Which spatial filter increases an image’s overall sharpness?
a. Low pass filter
b. High pass filter
c. Median filter
d. Gaussian filter
Answer: b. High pass filtereasyexamnotes+1
What is the main objective of image enhancement in AI?
a. Data compression
b. Human visual interpretation improvement
c. Edge detection
d. Pattern recognition
Answer: b. Human visual interpretation improvementsanfoundry+1
Which frequency domain filter is commonly used for image smoothing?
a. Butterworth low pass filter
b. High pass filter
c. Gaussian high pass filter
d. Laplacian filter
Answer: a. Butterworth low pass filterscribd
Which method is suitable for reducing Gaussian noise in images?
a. Median filter
b. Laplacian filter
c. Gaussian filter
d. Sobel filter
Answer: a. Median filterscribd
What is the primary purpose of Butterworth high pass filter in frequency domain image processing?
a. Reduce noise
b. Enhance contrast
c. Smooth image
d. Sharpen image
Answer: d. Sharpen imagescribd
Which color space is often preferred for digital image enhancement?
a. RGB
b. CMYK
c. HSV
d. LAB
Answer: c. HSVscribd+1
Which spatial domain method is used for enhancing edges and fine details?
a. Smoothing spatial filters
b. Sharpening spatial filters
c. Intensity transformation functions
d. Combined enhancement methods
Answer: b. Sharpening spatial filterseasyexamnotes+1
What is histogram equalization generally used for?
a. Blurring
b. Contrast adjustment
c. Image compression
d. Edge detection
Answer: b. Contrast adjustmentsanfoundry
Which AI technique is best for learning complex features in image enhancement tasks?
a. Support Vector Machine
b. Decision Tree
c. Convolutional Neural Network
d. Random Forest
Answer: c. Convolutional Neural Networkpsbtrainingacademy
Unsharp masking in image processing primarily involves:
a. Only blurring
b. Subtracting blurred image from original
c. Mask addition only
d. None of the above
Answer: b. Subtracting blurred image from originalsanfoundry
What is the role of non-linear filters, like the median filter, in enhancement?
a. Smoothing images
b. Sharpening edges
c. Reducing impulse noise
d. Enhancing colors
Answer: c. Reducing impulse noisesanfoundry+1
Adaptive histogram equalization differs from standard histogram equalization in that it:
a. Uses same histogram for whole image
b. Adaptively improves local contrast
c. Performs image segmentation
d. Only works on grayscale images
Answer: b. Adaptively improves local contrastsanfoundry+1
Which transform is NOT typically used in frequency domain image enhancement?
a. Fourier Transform
b. Discrete Cosine Transform
c. Laplace Transform
d. Wavelet Transform
Answer: c. Laplace Transformscribd+1
Which convolution kernel detects edges in images?
a. Box filter
b. Sobel operator
c. Gaussian kernel
d. Low pass filter
Answer: b. Sobel operatorscribd+1
Deep learning for image enhancement is effective because:
a. It always provides pixel-perfect accuracy
b. It learns hierarchical image features
c. It works only with binary images
d. It removes all noise
Answer: b. It learns hierarchical image featurespsbtrainingacademy
Which operation is often performed before thresholding in an AI-based image enhancement workflow?
a. Histogram specification
b. Smoothing
c. Color quantization
d. JPEG compression
Answer: b. Smoothingsanfoundry
Which of the following is an example of a sharpening spatial filter?
a. Mean filter
b. Laplacian filter
c. Gaussian filter
d. Median filter
Answer: b. Laplacian filtersanfoundry
An AI-enhanced image restoration process typically improves:
a. Image compression
b. Image segmentation
c. Image quality from degraded data
d. Feature detection only
Answer: c. Image quality from degraded datasanfoundry
CLAHE (Contrast Limited Adaptive Histogram Equalization) is applied to avoid:
a. Color distortion
b. Noise amplification
c. Loss of resolution
d. Edge detection
Answer: b. Noise amplificationscribd+1
Which filtering technique preserves edges while reducing noise?
a. Median filtering
b. Mean filtering
c. Gaussian filtering
d. Laplacian filtering
Answer: a. Median filteringscribd+1
In AI, which task does super-resolution address?
a. Denoising
b. Upscaling image quality
c. Color mapping
d. Feature matching
Answer: b. Upscaling image qualitypsbtrainingacademy+1
What does image normalization achieve in enhancement?
a. Removes color
b. Stabilizes mean and variance
c. Adds blur
d. Converts to grayscale
Answer: b. Stabilizes mean and variancescribd+1
Which function brings out details in dark regions for image enhancement?
a. Logarithmic transformation
b. Histogram equalization
c. Segmentation
d. Blurring
Answer: a. Logarithmic transformationscribd+1
In AI-powered enhancement, autoencoders are primarily used for:
a. Data compression
b. Feature visualization
c. Image denoising
d. Image splitting
Answer: c. Image denoisingpsbtrainingacademy+1
Which neural network layer is specific to image enhancement for capturing spatial features?
a. Fully connected
b. Convolutional
c. Recurrent
d. Dropout
Answer: b. Convolutionalpsbtrainingacademy
Why is the Laplacian filter used in enhancement?
a. Smooths the image
b. Reduces histogram spread
c. Highlights rapid intensity changes
d. Applies color mapping
Answer: c. Highlights rapid intensity changesscribd+1
Point processing enhancement operates on:
a. Image regions
b. Whole image
c. Individual pixels
d. Frequency domain
Answer: c. Individual pixelsscribd+1
What is gamma correction used for?
a. Edge sharpening
b. Non-linear brightness scaling
c. Smoothing
d. Compression
Answer: b. Non-linear brightness scalingscribd+1
Which statement best describes image enhancement in AI?
a. It creates new data
b. It modifies existing image characteristics for better perception
c. It always uses deep learning
d. It removes all noise
Answer: b. It modifies existing image characteristics for better perceptionscribd+1
Histogram stretching enhances an image by:
a. Compressing contrast
b. Expanding dynamic range
c. Reducing bandwidth
d. Transforming shapes
Answer: b. Expanding dynamic rangesanfoundry+1
Color image enhancement in AI typically involves manipulating:
a. Only red channel
b. All three color channels
c. Only green channel
d. Only blue channel
Answer: b. All three color channelssanfoundry
Image “sharpening” highlights:
a. Low-frequency details
b. High-frequency components
c. Uniform regions
d. Color differences only
Answer: b. High-frequency componentseasyexamnotes+1
Morphological operations for enhancement are mostly applicable to:
a. Color images
b. Gray-scale images
c. Binary images
d. 3D volumes
Answer: c. Binary imagesscribd+1
Piecewise-linear transformation aids in:
a. Noise rejection
b. Edge detection
c. Adjusting contrast in certain intensity ranges
d. Image segmentation
Answer: c. Adjusting contrast in certain intensity rangesscribd+1
What operation in AI enhancement improves image “visibility” under varying lighting?
a. Histogram specification
b. Gray-level slicing
c. Edge thinning
d. Sharpening filter
Answer: b. Gray-level slicingsanfoundry
Frequency domain enhancement modifies image:
a. Pixel intensities
b. Color spaces
c. Frequency coefficients
d. Region segments
Answer: c. Frequency coefficientsscribd+1
A convolutional neural network improves image enhancement by:
a. Manual feature selection
b. Learning spatial filters automatically
c. Reducing color depth
d. Encoding metadata
Answer: b. Learning spatial filters automaticallytestbook+1
Principal Component Analysis (PCA) in AI image enhancement helps:
a. Compress color range
b. Highlight variance and patterns
c. Blur the image
d. Only segment images
Answer: b. Highlight variance and patternsscribd+1
Gabor filters in image enhancement are primarily used for:
a. Edge detection
b. Texture analysis
c. Color correction
d. Image segmentation
Answer: b. Texture analysissanfoundry+1
What happens during “contrast stretching”?
a. Images are blurred
b. Intensity range is compressed
c. Intensity range is expanded
d. Only color images are processed
Answer: c. Intensity range is expandedscribd+1
In AI, GANs can enhance images by:
a. Blurring edges
b. Generating realistic textures and details
c. Adding random noise
d. Converting images to grayscale
Answer: b. Generating realistic textures and detailsgyansetu+1
The main use of domain transform in image enhancement is:
a. Edge-preserving smoothing
b. Color deepening
c. Histogram flattening
d. Morphological segmentation
Answer: a. Edge-preserving smoothingsanfoundry+1
Why are deep residual networks (ResNets) valuable for enhancement tasks?
a. They only classify images
b. They mitigate vanishing gradients and enable deeper learning
c. They compress images
d. They remove RGB channels
Answer: b. They mitigate vanishing gradients and enable deeper learningtestbook+1
Which is a limitation of AI-based image enhancement?
a. Realistic texture synthesis
b. Model overfitting or hallucination
c. Edge detection
d. Image segmentation
Answer: b. Model overfitting or hallucinationtestbook+1
A bilateral filter uniquely:
a. Removes all sharpness
b. Preserves edges while smoothing
c. Randomizes noise
d. Narrows dynamic range
Answer: b. Preserves edges while smoothingscribd+1
In AI-driven medical imaging, enhancement is crucial for:
a. Data compression
b. Improving diagnostic accuracy
c. File encryption
d. Image colorization
Answer: b. Improving diagnostic accuracypsbtrainingacademy
Deep learning enhancement models are typically evaluated using:
a. Peak Signal-to-Noise Ratio (PSNR)
b. File size
c. Compression ratio
d. Color palette
Answer: a. Peak Signal-to-Noise Ratio (PSNR)testbook+1

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