# Digital Image Processing Questions and Answers – Enhancement using Arithmetic Operations

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This set of Digital Image Processing Multiple Choice Questions & Answers (MCQs) focuses on “Enhancement using Arithmetic Operations”.

1. Which of the following is/are more commercially successful image enhancement method in mask mode radiography, an area under medical imaging?
b) Subtraction
c) Multiplication
d) Division

Explanation: In the given area of medical imaging, a mask of an X-ray image of a region of subject is captured using TV camera is subtracted from image of same region taken after injecting a contrast medium to the bloodstream. The subtraction result gives an enhanced detail of how a contrast medium propagates through the bloodstream.
This the best commercially successful method.

2. The subtraction operation results in areas that appear as dark shades of gray. Why?
a) Because the difference in such areas is little, that yields low value
b) Because the difference in such areas is high, that yields low value
c) Because the difference in such areas is high, that yields high value
d) None of the mentioned

Explanation: There remains a little change in some areas in the images to be subtracted that yields low value and so the result appears as dark shades of gray.

3. If the images are displayed using 8-bits, then, what is the range of the value of an image if the image is a result of subtraction operation?
a) 0 to 255
b) 0 to 511
c) -255 to 0
d) None of the mentioned

Explanation: The range of a result of a subtracted image is -255 m inimum to 255 max imum, if 8-bit channel is used to display the original images.
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4. The subtracted image needs to be scaled, if 8-bit channel is used to display the subtracted images. So, the method of adding 255 to each pixel and then dividing by 2, has certain lim its. What is/are those lim its?
a) Very complex method
b) Very difficult to implement
c) The truncation inherent in division by 2 causes loss in accuracy
d) All of the mentioned

Explanation: The given method is quite simple and easy to implement, however it has the lim itation of accuracy loss because of truncation inherent in division by 2 and also that it doesn’t ensure the full range usage.

5. Which of the following is/are the fundamental factors that need tight control for difference based inspection work?
a) Proper registration
b) Controlled illum ination
c) Noise levels should be low enough so that the variation due to noise won’t affect the difference value much
d) All of the mentioned

Explanation: Proper Registration does special marking into the product in case two images are identical so as the difference won’t create any sense.
Controlled Illum ination is important because changes in illum ination can affect dramatically the difference image values.
Noise levels of a difference image must low enough so that the variation due to noise won’t affect the difference value much.

6. When can two random variables be uncorrelated?
a) Their covariance is 0
b) Their covariance is 1
c) Their covariance is -1
d) None of the mentioned

Explanation: The covariance of two random variables x i and x j given by: E [(x i – m i) (x j – mj)], E {.} is expected value of the argument and m is the mean. If this covariance turns out to 0, the variables are uncorrelated.

7. In Image Averaging enhancement method assumptions are made for a noisy image g(x, y). What is/are those?
a) The noise is correlated at every pair of coordinate (x, y)
b) The noise has average value 1 at every pair of coordinate (x, y)
c) All of the mentioned
d) None of the mentioned

Explanation: In Image Averaging enhancement method assumptions are made for a noisy image g(x, y) that at every coordinate (x, y) the noise has 0 average value and must be uncorrelated.

8. The standard deviation ‘σ’ at any point in image averaging: σḡ(x, y) = 1/√K σɳ(x, y), where ḡ(x, y) is the average image formed by averaging K different noisy images and ɳ(x, y) is the noise added to an original image f(x, y). What is the relation between K and the variability of the pixel values at each location (x, y)?
a) Increase in K, decreases the noise of pixel values
b) Increase in K, increases the noise of pixel values
c) Decrease in K, decreases the noise of pixel values
d) Decrease in K, increases the noise of pixel values

Explanation: As K increases, E {ḡ(x, y)} the expected value approaches f(x, y) the original image, i.e. decreasing the noise component.

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