Second order derivative Use For Image Enhancement MCQ [Free PDF]

11. Which of the following gives an expression for a high boost filtered image file, if f represents an image, f blurred version off, fs unsharp mask filtered image, and A ≥ 1?

A. fhb = (A – 1) f(x, y) + f(x, y) – f x, y)
B. fhb = A f(x, y) – f(x,y)
C. fhb = (A – 1) f(x, y) + fs(x, y)
D. All of the mentioned

Answer: D

A high-boost filter is a generalized form of unsharp mask and is given by:
fhb = A f(x, y) – f (x, y)
Or, fhb = (A – 1) f(x, y) + f(x, y) – f(x, y), that can be written as
fhb = (A – 1) f(x, y) + fs(x, y), where fs(x, y) = f(x, y) – f (x, y).

12. If we use a Laplacian to obtain a sharp image for unsharp mask filtered image fs(x, y) of f(x, y) as the input image, and if the center coefficient of the Laplacian mask is negative then, which of the following expression gives the high boost filtered image fhb, if ∇2 f represent Laplacian?

A. fhb = A f(x, y) – ∇2 f(x,y)
B. fhb = A f(x, y) + ∇2 f(x,y)
C. fhb = ∇2 f(x,y)
D. None of the mentioned

Answer: A

If Laplacian is used to obtain a sharp image for an unsharp mask filtered image, then

fhb = A f(x, y) – ∇2 f(x,y)

13. “For a very large value of A, a high boost filtered image is approximately equal to the original image”. State whether the statement is true or false?

A. True
B. False

Answer: A

As the value of A increases, the sharpening process contribution becomes less important and so at some very large value A, the contribution becomes almost negligible and so the high boost filtered image is approximately equal to the original image.

14. Subtracting Laplacian from an image is proportional to which of the following?

A. Unsharp masking
B. Box filter
C. Median filter
D. None of the mentioned

Answer: A

subtracting Laplacian from an image gives:
f(x,y)- ∇2 f(x,y) = f(x, y) – [f(x + 1, y) + f(x – 1, y) + f(x, y + 1) + f(x, y – 1) – 4f(x, y)]
That on calculation gives 5[1.2 f(x, y) – f ̅(x, y)] ≈ 5[f(x, y) – f(x, y)]
Where f(x, y) – f(x, y) is the unsharp masking definition.

15. A First derivative in image processing is implemented using which of the following given operator(s)?

A. Magnitude of the Gradient vector
B. The Laplacian
C. All of the mentioned
D. None of the mentioned

Answer: A

The magnitude of the Gradient vector is used for the implementation of the first derivative in image processing, while Laplacian is for second-order implementation in image processing.

16. If for an image function f(x, y), the magnitude of gradient vector is given by: mag(∇f)=[G2x+G2y] (1/2), then which of the following fact is correct?

A. The component of the Gradient vector is a linear operator and also the magnitude of the vector
B. The component of the Gradient vector is a linear operator, but the magnitude is not
C. The component of the Gradient vector are nonlinear operators and also the magnitude of the vector
D. The component of the Gradient vector are nonlinear operators, but the magnitude is not

Answer: B

The component of the Gradient vector is a linear operator because these are derivatives but the magnitude of the vector is not because of the squaring and square root operations.

17. What is the sum of the coefficient of the mask defined using gradient?

A. 1
B. -1
C. 0
D. None of the mentioned

Answer: C

Since the first-order derivative of a digital function must be zero in the areas of constant grey values. So, the mask using gradient has a sum of 0, so to produce a zero result if applied on constant gray level areas.

18. The gradient is used in which of the following area(s)?

A. To aid humans in the detection of defects
B. As a preprocessing step for automated inspections
C. All of the mentioned
D. None of the mentioned

Answer: C

Gradient has usage in both human analyses as well as a preprocessing step for automated inspections.

19. Gradients have some important features. Which of the following are/are some of them?

A. Enhancing small discontinuities in an otherwise flat gray field
B. Enhancing prominent edges
C. All of the mentioned
D. None of the mentioned

Answer: C

Since gradients are used in first-order derivative image enhancement that enhances the discontinuities except for in flat areas and produces a thick edge for constant slope ramp. So, Gradient has all the mentioned features.

20. An image has significant edge details. Which of the following fact(s) is/are true for the gradient image and the Laplacian image of the same?

A. The gradient image is brighter than the Laplacian image
B. The gradient image is brighter than the Laplacian image
C. Both the gradient image and the Laplacian image have equal values
D. None of the mentioned

Answer: A

Because the gradient enhances prominent edges better than Laplacian, so, the Gradient image with significant edge detail has a higher value than the Laplacian image.

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