WorksheetsMCQs on Image Degradation and Restoration
Total questions: 40
Worksheet time: 20mins
What does the image degradation process typically involve?
Blur and noise
Contrast enhancement
Image segmentation
Edge detection
Which of the following is NOT a type of noise in images?
Gaussian noise
Salt and pepper noise
Speckle noise
Gradient noise
Which domain is commonly used for periodic noise reduction?
Time domain
Spatial domain
Frequency domain
Color domain
What is the purpose of Wiener filtering?
Sharpen the image
Reduce salt noise
Restore image in presence of noise
Segment the image
What is the key assumption in inverse filtering?
Known noise model
Known degradation function
Constant image intensity
Zero padding
Which of the following noise models is characterized by random occurrences of black and white pixels?
Gaussian noise
Rayleigh noise
Salt and pepper noise
Poisson noise
What does the degradation model H(u,v) represent in frequency domain?
Blur kernel
Degradation function
Noise function
Edge function
Which filter works better in the presence of additive noise?
Mean filter
Inverse filter
Wiener filter
Laplacian filter
What is the main drawback of inverse filtering?
Too expensive
Sensitive to noise
Hard to implement
Color distortion
Which filtering technique is used in spatial domain restoration?
Inverse filtering
Butterworth filtering
Mean filtering
Wiener filtering
Which function models the noise statistically in Wiener filtering?
Histogram
Autocorrelation
Power spectrum
Noise power spectrum
What does a degradation function model in image processing?
Lighting variations
Color spectrum
Blurring process
Histogram changes
Median filter is effective in removing which type of noise?
Gaussian
Speckle
Salt and pepper
Poisson
In frequency domain, periodic noise appears as:
Blur
Isolated dots
Ripples
Dark bands
Why can't inverse filtering always recover the original image?
Noise is random
Edges are lost
H(u,v) may be zero
Colors distort
Which of these is a linear spatial filter?
Median filter
Mean filter
Geometric mean filter
Wiener filter
Wiener filtering minimizes the:
Blur
Noise
Mean squared error
Sharpening effect
In the restoration model g(x,y) = h(x,y)*f(x,y) + η(x,y), η represents:
Filter
Noise
Image
Convolution
What is the frequency response of a degradation model used for?
Color correction
Noise estimation
Restoration filtering
Histogram stretching
A filter that preserves edges while reducing noise is:
Average filter
Median filter
Gaussian filter
Inverse filter
What does spatial filtering operate on?
Image pixels
Image frequency
Color model
Image metadata
What is the ideal restoration filter if noise is absent?
Wiener
Median
Inverse
Low-pass
Periodic noise reduction in frequency domain uses:
Low-pass filter
Band-reject filter
Histogram equalization
Edge detector
Speckle noise is commonly found in:
Satellite images
MRI images
Text documents
Barcodes
Which is a non-linear filter?
Gaussian
Median
Wiener
Inverse
Noise power is assumed to be:
Infinite
Constant
Zero
Random
Inverse filtering fails when:
H(u,v) = 0
Image is noisy
Color is saturated
Edges are weak
Gaussian noise follows which distribution?
Uniform
Normal
Poisson
Rayleigh
Which technique estimates the degradation function?
Blind deconvolution
Inverse filtering
Wiener filtering
Histogram equalization
The model g(x,y) = f(x,y) + η(x,y) assumes:
No degradation
Noise only
Convolution
Edge detection
Degradation is usually modeled as:
Addition
Subtraction
Convolution
Multiplication
Which of the following is used to analyze noise in frequency domain?
Histogram
DFT
Bit plane slicing
JPEG compression
Wiener filter requires knowledge of:
Only image
Only noise
Image and noise statistics
Only blur
A Butterworth filter is used in:
Spatial smoothing
Frequency domain restoration
Color balancing
Edge enhancement
The output of inverse filter is sensitive to:
Blur
Degradation model
Noise
Resolution
Which is not an effect of noise?
Loss of detail
Increased contrast
Graininess
Pixel variation
Which filter combines image and noise power?
Mean filter
Gaussian filter
Wiener filter
Inverse filter
A point spread function describes:
Color distribution
Blur characteristics
Noise intensity
Histogram shape
An ideal low-pass filter in frequency domain can cause:
Ringing
Blurring
Edge enhancement
Noise increase
Histogram equalization is primarily used for:
Noise removal
Restoration
Enhancement
Compression
