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Welcome to the Image Enhancement MCQs Page

Dive deep into the fascinating world of Image Enhancement with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Image Enhancement, a crucial aspect of Digital Image Processing (DIP). In this section, you will encounter a diverse range of MCQs that cover various aspects of Image Enhancement, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Digital Image Processing (DIP).

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Check out the MCQs below to embark on an enriching journey through Image Enhancement. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Digital Image Processing (DIP).

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Image Enhancement. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Image Enhancement MCQs | Page 6 of 11

Explore more Topics under Digital Image Processing (DIP)

Q51.
The transformation s = T(r) producing a gray level s for each pixel value r of input image. Then, if the T(r) is satisfying 0 ≤ T(r) ≤ 1 in interval 0 ≤ r ≤ 1, what does it signifies?

a.

It guarantees the existence of inverse transformation

b.

It is needed to restrict producing of some inverted gray levels in output

c.

It guarantees that the output gray level and the input gray level will be in same range

d.

All of the mentioned

Discuss
Answer: (c).It guarantees that the output gray level and the input gray level will be in same range
Q52.
What is the full form for PDF, a fundamental descriptor of random variables i.e. gray values in an image?

a.

Pixel distribution function

b.

Portable document format

c.

Pel deriving function

d.

Probability density function

Discuss
Answer: (d).Probability density function
Q53.
What is the full form of CDF?

a.

Cumulative density function

b.

Contour derived function

c.

Cumulative distribution function

d.

None of the mentioned

Discuss
Answer: (c).Cumulative distribution function
Q54.
For the transformation T(r) = [∫0^r pr(w) dw], r is gray value of input image, pr(r) is PDF of random variable r and w is a dummy variable. If, the PDF are always positive and that the function under integral gives the area under the function, the transformation is said to be __________

a.

Single valued

b.

Monotonically increasing

c.

All of the mentioned

d.

None of the mentioned

Discuss
Answer: (c).All of the mentioned
Q55.
The transformation T (rk) = ∑k(j=0) nj /n, k = 0, 1, 2, …, L-1, where L is max gray value possible and r-k is the kth gray level, is called _______

a.

Histogram linearization

b.

Histogram equalization

c.

All of the mentioned

d.

None of the mentioned

Discuss
Answer: (c).All of the mentioned
Q56.
If the histogram of same images, with different contrast, are different, then what is the relation between the histogram equalized images?

a.

They look visually very different from one another

b.

They look visually very similar to one another

c.

They look visually different from one another just like the input images

d.

None of the mentioned

Discuss
Answer: (b).They look visually very similar to one another
Q57.
The technique of Enhancement that has a specified Histogram processed image as result, is called?

a.

Histogram Linearization

b.

Histogram Equalization

c.

Histogram Matching

d.

None of the mentioned

Discuss
Answer: (c).Histogram Matching
Q58.
In Histogram Matching r and z are gray level of input and output image and p stands for PDF, then, what does pz(z) stands for?

a.

Specific probability density function

b.

Specified pixel distribution function

c.

Specific pixel density function

d.

Specified probability density function

Discuss
Answer: (d).Specified probability density function
Q59.
Inverse transformation plays an important role in which of the following Histogram processing Techniques?

a.

Histogram Linearization

b.

Histogram Equalization

c.

Histogram Matching

d.

None of the mentioned

Discuss
Answer: (c).Histogram Matching
Q60.
In Histogram Matching or Specification, z = G^-1[T(r)], r and z are gray level of input and output image and T & G are transformations, to confirm the single value and monotonous of G^-1 what of the following is/are required?

a.

G must be strictly monotonic

b.

G must be strictly decreasing

c.

All of the mentioned

d.

None of the mentioned

Discuss
Answer: (a).G must be strictly monotonic
Page 6 of 11

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