# Digital Image Processing Questions and Answers – Spatial Filtering

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This set of Digital Image Processing Multiple Choice Questions & Answers (MCQs) focuses on “Spatial Filtering”.

1. 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
c) Template
d) All of the mentioned

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.

2. 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

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.

3. 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)

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).
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4. 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

Explanation: Computation of variance as well as median comes under nonlinear operation.

5. 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

Explanation: Computation of median gray-level value in the neighborhood is the basic function of nonlinear filter for noise reduction.

6. 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

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.

7. 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

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.

8. 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?
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

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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