Image Enhancement in Digital Image Processing MCQ [Free PDF] - Objective Question Answer for Image Enhancement Quiz

91. 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

Answer: A

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

92. A filter is applied to an image whose response is independent of the direction of discontinuities in the image. The filter is/are ________

A. Isotropic filters
B. Box filters
C. Median filter
D. All of Mentioned

Answer: A

A filter is applied to an image whose response is independent of the direction of discontinuities in the image. The filter is/are isotropic filters.

The isotropic filter is rotation invariant because it has the same response when applied to the image first and then after rotating the image.

93. In isotropic filtering, which of the following is/are the simplest isotropic derivative operator?

A. Laplacian
B. Gradient
C. All of Mentioned
D. None of the mentioned

Answer: A

Isotropic filtering is an example of a second-order derivative for enhancement and uses Laplacian as the simplest derivative operator, while gradient is used with first derivatives.

94. The Laplacian is which of the following operator?

A. Nonlinear operator
B. Order-Statistic operator
C. Linear operator
D. None of the mentioned

Answer: C

Derivatives of any order are linear operations and since Laplacian is the simplest isotropic derivative operator, so is a linear operator.

Order-Statistics operators are nonlinear operators.

95. A Laplacian for an image f(x, y) is given by ________

A. [f(x + 1, y) + f(x – 1, y) – 2f(x, y)] and [f(x, y + 1) + f(x, y – 1) – 2f(x, y)] respectively
B. [f(x + 1, y + 1) + f(x, y – 1) – 2f(x, y)] and [f(x , y + 1) + f(x – 1, y) – 2f(x, y)] respectively
C. [f(x, y + 1) + f(x, y – 1) – 2f(x, y)] and [f(x + 1, y) + f(x – 1, y) – 2f(x, y)] respectively
D. None of the mentioned

Answer: A

For a Laplacian given by∇2 f

Applying second order derivative in x direction (∂2 f)/∂x2 = [f(x + 1, y) + f(x – 1, y) – 2f(x, y)], and

Applying second order derivative in y direction (∂2 f)/∂y2 = [f(x, y + 1) + f(x, y – 1) – 2f(x, y)].

96. The Laplacian ∇2 f=[f(x + 1, y) + f(x – 1, y) + f(x, y + 1) + f(x, y – 1) – 4f(x, y)], gives an isotropic result for rotations in increment by what degree?

A. 90°
B. 0°
C. 45°
D. None of the mentioned

Answer: A

The given Laplacian gives isotropic results for 90° incremental rotations.

97. The Laplacian incorporated with diagonal directions, i.e. ∇2 f=[f(x + 1, y) + f(x – 1, y) + f(x, y + 1) + f(x, y – 1) – 8f(x, y)], gives an isotropic result for rotations in increment by what degree?

A. 90°
B. 0°
C. 45°
D. None of the mentioned

Answer: C

The given Laplacian since includes the diagonal direction, so, gives an isotropic result for 45° incremental rotations.

98. Applying Laplacian has which of the following result(s)?

A. Produces images having greyish edge lines
B. Produces image having a featureless background
C. All of Mentioned
D. None of the mentioned

Answer: C

Since Laplacian is a derivative operator, so, highlights the gray-level discontinuities in an image and deemphasizes areas with slowly varying gray levels. Hence, produces images having greyish edge lines superimposed on the featureless background.

99. Applying Laplacian produces an image having featureless background which is recovered maintaining the sharpness of the Laplacian operation by either adding or subtracting it from the original image depending upon the Laplacian definition used. Which of the following is true based on the above statement?

A. If the definition used has a negative center coefficient, then subtraction is done
B. If the definition used has a positive center coefficient, then subtraction is done
C. If the definition used has a negative center coefficient, then the addition is done
D. None of the mentioned

Answer: A

Applying Laplacian produces an image having featureless background which is recovered maintaining the sharpness of the Laplacian operation using the original image either added if the Laplacian definition used has a positive center coefficient or subtracting the result from the original image if has a negative center coefficient.

100. A mask of size 3*3 is formed using Laplacian including diagonal neighbors that have a central coefficient of 9. Then, what would be the central coefficient of the same mask if it is made without diagonal neighbors?

A. 5
B. -5
C. 8
D. -8

Answer: A

The mask is formed by eliminating diagonal neighbors i.e. 4f(x, y) since each diagonal contains a -2f(x, y), the mask has 5 as its central coefficient.

101. Which of the following mask(s) is/are used to sharpen images by subtracting a blurred version of the original image from the original image itself?

A. Unsharp mask
B. High-boost filter
C. All of Mentioned
D. None of the mentioned

Answer: C

Unsharp mask sharpens images by subtracting a blurred version of the original image from the original image itself.
A high-boost filter is a generalized form of unsharp mask.

102. 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 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).

102. 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)

103. “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.

104. 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.

105. The 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 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.

106. 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.

107. 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.

108. 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 Mentioned
D. None of the mentioned

Answer: C

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

109. 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 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.

110. 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

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

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