500+ MCQ’s Questions of digital Image Processing mcq question 2021 – Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image.
Here’s the list of chapters on the “Digital Image Processing ” subject covering 100+ topics. You can practice the MCQs chapter by chapter . digital image processing mcq questions
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- Basic of Digital Image Processing
- Digital Image Fundamentals
- Intensity Transformations and Spatial Filtering
- Filtering in Frequency Domain
- Image Restoration and Reconstruction
- Color Image Processing
- Image Compression
- Morphological Image Processing
- Image Segmentation
- Representation and Description
- Wavelet based Image Processing
- Image Enhancement
- Object Recognition
Smoothing Spacial Filters
1. The output of a smoothing, linear spatial filtering is a ____________ of the pixels contained in the neighbourhood of the filter mask.
a) Sum
b) Product
c) Average
d) Dot Product
Answer: c
Explanation: Smoothing is simply the average of the pixels contained in the neighbourhood.
2. Averaging filters is also known as ____________ filter.
a) Low pass
b) High pass
c) Band pass
d) None of the Mentioned
Answer: a
Explanation: Averaging filters is also known as Low pass filters.
3. What is the undesirable side effects of Averaging filters?
a) No side effects
b) Blurred image
c) Blurred edges
d) Loss of sharp transitions
Answer: c
Explanation: Blue edges is the undesirable side effect of Averaging filters.
4. A spatial averaging filter in which all coefficients are equal is called _______________.
a) Square filter
b) Neighbourhood
c) Box filter
d) Zero filter
Answer: c
Explanation: It is called a Box filter.
5. Which term is used to indicate that pixels are multiplied by different coefficients?
a) Weighted average
b) Squared average
c) Spatial average
d) None of the Mentioned
Answer: a
Explanation: It is called weighted average since more importance(weight) is given to some pixels
6. The non linear spacial filters whose response is based on ordering of the pixels contained is called _____________.
a) Box filter
b) Square filter
c) Gaussian filter
d) Order-statistic filter
Answer: d
Explanation: It is called Order-statistic filter.
7. Impulse noise in Order-statistic filter is also called as _______________
a) Median noise
b) Bilinear noise
c) Salt and pepper noise
d) None of the Mentioned
Answer: c
Explanation: It is called salt-and-pepper noise because of its appearance as white and black dots superimposed on an image.
8. Best example for a Order-statistic filter is ____________________
a) Impulse filter
b) Averaging filter
c) Median filter
d) None of the Mentioned
Answer: c
Explanation: Median filter is the best known Order-statistic filter.
9. What does “eliminated” refer to in median filter?
a) Force to average intensity of neighbours
b) Force to median intensity of neighbours
c) Eliminate median value of pixels
d) None of the Mentioned
Answer: b
Explanation: It refers to forcing to median intensity of neighbors.
10. Which of the following is best suited for salt-and-pepper noise elimination?
a) Average filter
b) Box filter
c) Max filter
d) Median filter
Answer: d
Explanation: Median filter is better suited than average filter for salt-and-pepper noise elimination.
Smoothing Linear Spatial Filters
1. Smoothing filter is used for which of the following work(s)?
a) Blurring
b) Noise reduction
c) All of the mentioned
d) None of the mentioned
Answer: c
Explanation: Smoothing filter is used for blurring and noise reduction.
2. The response of the smoothing linear spatial filter is/are __________
a) Sum of image pixel in the neighborhood filter mask
b) Difference of image in the neighborhood filter mask
c) Product of pixel in the neighborhood filter mask
d) Average of pixels in the neighborhood of filter mask
Answer: d
Explanation: The average of pixels in the neighborhood of filter mask is simply the output of the smoothing linear spatial filter.
3. Which of the following filter(s) results in a value as average of pixels in the neighborhood of filter mask.
a) Smoothing linear spatial filter
b) Averaging filter
c) Lowpass filter
d) All of the mentioned
Answer: d
Explanation: The output as an average of pixels in the neighborhood of filter mask is simply the output of the smoothing linear spatial filter also known as averaging filter and lowpass filter.
4. What is/are the resultant image of a smoothing filter?
a) Image with high sharp transitions in gray levels
b) Image with reduced sharp transitions in gray levels
c) All of the mentioned
d) None of the mentioned
Answer: b
Explanation: Random noise has sharp transitions in gray levels and smoothing filters does noise reduction
5. At which of the following scenarios averaging filters is/are used?
a) In the reduction of irrelevant details in an image
b) For smoothing of false contours
c) For noise reductions
d) All of the mentioned
Answer: d
Explanation: Averaging filter or smoothing linear spatial filter is used: for noise reduction by reducing the sharp transitions in gray level, for smoothing false contours that arises because of use of insufficient number of gray values and for reduction of irrelevant data i.e. the pixels regions that are small in comparison of filter mask.
6. A spatial averaging filter having all the coefficients equal is termed _________
a) A box filter
b) A weighted average filter
c) A standard average filter
d) A median filter
Answer: a
Explanation: An averaging filter is termed as box filter if all the coefficients of spatial averaging filter are equal.
7. What does using a mask having central coefficient maximum and then the coefficients reducing as a function of increasing distance from origin results?
a) It results in increasing blurring in smoothing process
b) It results to reduce blurring in smoothing process
c) Nothing with blurring occurs as mask coefficient relation has no effect on smoothing process
d) None of the mentioned
Answer: a
Explanation: Use of a mask having central coefficient maximum and then the coefficients reducing as a function of increasing distance from origin is a strategy to reduce blurring in smoothing process.
8. What is the relation between blurring effect with change in filter size?
a) Blurring increases with decrease of the size of filter size
b) Blurring decrease with decrease of the size of filter size
c) Blurring decrease with increase of the size of filter size
d) Blurring increases with increase of the size of filter size
Answer: d
Explanation: Using a size 3 filter 3*3 and 5*5 size squares and other objects shows a significant blurring with respect to object of larger size.
The blurring gets more pronounced while using filter size 5, 9 and so on.
Smoothing Nonlinear Spatial Filter
- Which of the following filter(s) has the response in which the central pixel value is replaced by value defined by ranking the pixel in the image encompassed by filter?
a) Order-Statistic filters
b) Non-linear spatial filters
c) Median filter
d) All of the mentioned
Answer: d
Explanation: An Order-Statistic filters also called non-linear spatial filters, response is based on ranking the pixel in the image encompassed by filter that replaces the central pixel value. A Median filter is an example of such filters.
- Is it true or false that “the original pixel value is included while computing the median using gray-levels in the neighborhood of the original pixel in median filter case”?
a) True
b) False
Answer: a
Explanation: A median filter the pixel value is replaced by median of the gray-level in the neighborhood of that pixel and also the original pixel value is included while computing the median.
- Two filters of similar size are used for smoothing image having impulse noise. One is median filter while the other is a linear spatial filter. Which would the blurring effect of both?
a) Median filter effects in considerably less blurring than the linear spatial filters
b) Median filter effects in considerably more blurring than the linear spatial filters
c) Both have the same blurring effect
d) All of the mentioned
Answer: a
Explanation: For impulse noise, median filter is much effective for noise reduction and causes considerably less blurring than the linear spatial filters.
- An image contains noise having appearance as black and white dots superimposed on the image. Which of the following noise(s) has the same appearance?
a) Salt-and-pepper noise
b) Gaussian noise
c) All of the mentioned
d) None of the mentioned
Answer: c
Explanation: An impulse noise has an appearance as black and white dots superimposed on the image. This is also known as Salt-and-pepper noise.
- While performing the median filtering, suppose a 3*3 neighborhood has value (10, 20, 20, 20, 15, 20, 20, 25, 100), then what is the median value to be given to the pixel under filter?
a) 15
b) 20
c) 100
d) 25
Answer: b
Explanation: The values are first sorted and so turns out to (10, 15, 20, 20, 20, 20, 20, 25, and 100). For a 3*3 neighborhood the 5th largest value is the median, and so is 20.
- Which of the following are forced to the median intensity of the neighbors by n*n median filter?
a) Isolated cluster of pixels that are light or dark in comparison to their neighbors
b) Isolated cluster of pixels whose area is less than one-half the filter area
c) All of the mentioned
d) None of the mentioned
Answer: c
Explanation: The isolated cluster pixel value doesn’t come as a median value and since are either are light or dark as compared to neighbors, so are forced with median intensity of neighbors that aren’t even close to their original value and so are sometimes termed “eliminated”.
If the area of such isolated pixels are < n2/2, that is again the pixel value won’t be a median value and so are eliminated.
Larger cluster pixels value are more pronounced to be a median value, so are considerably less forced to median intensity.
- Which filter(s) used to find the brightest point in the image?
a) Median filter
b) Max filter
c) Mean filter
d) All of the mentioned
Answer: b
Explanation: A max filter gives the brightest point in an image and so is used.
- The median filter also represents which of the following ranked set of numbers?
a) 100th percentile
b) 0th percentile
c) 50th percentile
d) None of the mentioned
Answer: c
Explanation: Since the median filter forces median intensity to the pixel which is almost the largest value in the middle of the list of values as per the ranking, so represents a 50th percentile ranked set of numbers.
- Which of the following filter represents a 0th percentile set of numbers?
a) Max filter
b) Mean filter
c) Median filter
d) None of the mentioned
Answer: d
Explanation: A min filter since provides the minimum value in the image, so represents a 0th percentile set of numbers.
Spatial Filtering
- In neighborhood operations working is being done with the value of image pixel in the neighborhood and the corresponding value of a subimage that has same dimension as neighborhood. The subimage is referred as _
a) Filter
b) Mask
c) Template
d) All of the mentioned
Answer: d
Explanation: Working in neighborhood operations is done with the value of a subimage having same dimension as neighborhood corresponding to the value in the image pixel. The subimage is called as filter, mask, template, kernel or window.
- The response for linear spatial filtering is given by the relationship __
a) Sum of filter coefficient’s product and corresponding image pixel under filter mask
b) Difference of filter coefficient’s product and corresponding image pixel under filter mask
c) Product of filter coefficient’s product and corresponding image pixel under filter mask
d) None of the mentioned
Answer: a
Explanation: In spatial filtering the mask is moved from point to point and at each point the response is calculated using a predefined relationship. The relationship in linear spatial filtering is given by: the Sum of filter coefficient’s product and corresponding image pixel in area under filter mask.
- In linear spatial filtering, what is the pixel of the image under mask corresponding to the mask coefficient w (1, -1), assuming a 3*3 mask?
a) f (x, -y)
b) f (x + 1, y)
c) f (x, y – 1)
d) f (x + 1, y – 1)
Answer: d
Explanation: The pixel corresponding to mask coefficient (a 3*3 mask) w (0, 0) is f (x, y), and so for w (1, -1) is f (x + 1, y – 1).
- Which of the following is/are a nonlinear operation?
a) Computation of variance
b) Computation of median
c) All of the mentioned
d) None of the mentioned
Answer: c
Explanation: Computation of variance as well as median comes under nonlinear operation.
- Which of the following is/are used as basic function in nonlinear filter for noise reduction?
a) Computation of variance
b) Computation of median
c) All of the mentioned
d) None of the mentioned
Answer: b
Explanation: Computation of median gray-level value in the neighborhood is the basic function of nonlinear filter for noise reduction.
- In neighborhood operation for spatial filtering if a square mask of size n*n is used it is restricted that the center of mask must be at a distance ≥ (n – 1)/2 pixels from border of image, what happens to the resultant image?
a) The resultant image will be of same size as original image
b) The resultant image will be a little larger size than original image
c) The resultant image will be a little smaller size than original image
d) None of the mentioned
Answer: c
Explanation: If the center of mask must be at a distance ≥ (n – 1)/2 pixels from border of image, the border pixels won’t get processed under mask and so the resultant image would be of smaller size.
- Which of the following method is/are used for padding the image?
a) Adding rows and column of 0 or other constant gray level
b) Simply replicating the rows or columns
c) All of the mentioned
d) None of the mentioned
Answer: c
Explanation: In neighborhood operation for spatial filtering using square mask, padding of original image is done to obtain filtered image of same size as of original image done, by adding rows and column of 0 or other constant gray level or by replicating the rows or columns of the original image.
- In neighborhood operation for spatial filtering using square mask of n*n, which of the following approach is/are used to obtain a perfectly filtered result irrespective of the size?
a) By padding the image
b) By filtering all the pixels only with the mask section that is fully contained in the image
c) By ensuring that center of mask must be at a distance ≥ (n – 1)/2 pixels from border of image
d) None of the mentioned
Answer: c
Explanation: By ensuring that center of mask must be at a distance ≥ (n – 1)/2 pixels from border of image, the resultant image would be of smaller size but all the pixels would be the result of the filter processing and so is a fully filtered result.
In the other approach like padding affect the values near the edges that gets more prevalent with mask size increase, while the another approach results in the band of pixels near border that gets processed with partial filter mask. So, not a fully filtered case.
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