# Digital Image Processing Questions and Answers – Use of First Order Derivative for Enhancement

This set of Digital Image Processing Question Bank focuses on “Use of First Order Derivative for Enhancement”.

1. “For 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

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

2. Subtracting Laplacian from an image is proportional to which of the following?
b) Box filter
c) Median filter
d) None of the mentioned

Explanation: 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.

3. A First derivative in image processing is implemented using which of the following given operator(s)?
b) The Laplacian
c) All of the mentioned
d) None of the mentioned

Explanation: Magnitude of Gradient vector is used for implementation of first derivative in image processing, while Laplacian is for second order implementation in image processing.

4. 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 Gradient vector are linear operator and also the magnitude of the vector
b) The component of Gradient vector are linear operator, but the magnitude are not
c) The component of Gradient vector are nonlinear operator and also the magnitude of the vector
d) The component of Gradient vector are nonlinear operator, but the magnitude are not

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

5. What is the sum of the coefficient of the mask defined using gradient?
a) 1
b) -1
c) 0
d) None of the mentioned

Explanation: Since, 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 0, so to produce a zero result if applied on constant gray level areas.
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6. Gradient is used in which of the following area(s)?
a) To aid humans in detection of defects
b) As a preprocessing step for automated inspections
c) All of the mentioned
d) None of the mentioned

Explanation: Gradient has a usage in both human analysis as well as a preprocessing step for automated inspections.

7. Gradient have some important features. Which of the following is/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

Explanation: Since gradient are used in fist order derivative image enhancement that enhances the discontinuities except for in flat areas and produces thick edge for constant slope ramp. So, Gradient has all the mentioned features.

8. 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 has equal values
d) None of the mentioned

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

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