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

fhb = (A – 1) f(x, y) + f(x, y) – f x, y)

fhb = (A – 1) f(x, y) + fs(x, y)

If r be the gray-level of image before processing and s after processing then which expression defines the power-law transformation, for the gray-level in the range [0, L-1]?

s = c log (1 + r), c is a constant and r ≥ 0

s = crᵞ, c and ᵞ are positive constants

s = c log (1 + r), c is a constant and r ≥ 0

The power-law transformation is given as: s = crᵞ, c and ᵞ are positive constants, and r is the gray-level of image before processing and s after processing. What happens if we increase the gamma value from 0.3 to 0.7?

The contrast decreases and the detail increases

The contrast increases and the detail increases

The contrast decreases and the detail decreases

The contrast increases and the detail decreases

The contrast decreases and the detail increases

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?

It guarantees the existence of inverse transformation

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

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

A bright image will have what kind of histogram, when the histogram, h(rk) = nk, rk the kth gray level and nk total pixels with gray level rk, is plotted nk versus rk?

The histogram that is narrow and centered toward the middle of gray scale

The histogram that are concentrated on the dark side of gray scale

The histogram whose component are biased toward high side of gray scale

The histogram that is narrow and centered toward the middle of gray scale

The histogram that covers wide range of gray scale and the distribution of pixel is approximately uniform

A low contrast image will have what kind of histogram when, the histogram, h(rk) = nk, rk the kth gray level and nk total pixels with gray level rk, is plotted nk versus rk?

The histogram that covers wide range of gray scale and the distribution of pixel is approximately uniform

The histogram that are concentrated on the dark side of gray scale

The histogram whose component are biased toward high side of gray scale

The histogram that is narrow and centered toward the middle of gray scale

The histogram that covers wide range of gray scale and the distribution of pixel is approximately uniform

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?

Specific probability density function

Specified pixel distribution function

Specific pixel density function

Specified probability density function

Which of the following fact is true for the masks that includes diagonal neighbors than the masks that doesn’t?

Both masks have same sharpness result

Mask that excludes diagonal neighbors has more sharpness than the masks that doesn’t

Mask that includes diagonal neighbors has more sharpness than the masks that doesn’t

Both masks have same sharpness result

Using gray-level transformation, the basic function power-law deals with which of the following transformation?

nth and nth root transformations

log and inverse-log transformations

negative and identity transformations

nth and nth root transformations

If we use a Laplacian to obtain sharp image for unsharp mask filtered image fs(x, y) of f(x, y) as 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?

fhb = A f(x, y) – ∇^2 f(x,y)

How aliasing does corrupts the sampled image?

By removing some frequency components from the sampled function

By introducing additional frequency components to the sampled function

By removing some frequency components from the sampled function

How can one reduce the aliasing effect on an image?

By increasing the high-frequency components of image by blurring the image

By reducing the high-frequency components of image by blurring the image

By increasing the high-frequency components of image by blurring the image

By reducing the high-frequency components of image by clarifying the image

By increasing the high-frequency components of image by clarifying the image

Why is scaling of Laplacian filtered images necessary?

Because it contain high positive values

Because it contain high negative value

Because it contain both positive and negative values

An enhanced image can be obtained as: g(x,y)=f(x,y)-∇^2 f(x,y), where Laplacian is being subtracted from f(x, y) the input image. What does this conclude?

That the center spike would be negative

That the immediate neighbors of center spike would be positive.

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

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

It guarantees the existence of inverse transformation

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

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

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 limits. What is/are those limits?

The truncation inherent in division by 2 causes loss in accuracy

What is the full form of CDF?

Cumulative distribution function

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)?

Increase in K, increases the noise of pixel values

Increase in K, decreases the noise of pixel values

Increase in K, increases the noise of pixel values

Decrease in K, decreases the noise of pixel values

Decrease in K, increases the noise of pixel values

The domain that refers to image plane itself and the domain that refers to Fourier transform of an image is/are :

Frequency domain and Spatial domain respectively

Spatial domain and Frequency domain respectively

Frequency domain and Spatial domain respectively

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

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

It guarantees the existence of inverse transformation

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

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

If h(rk) = nk, rk the kth gray level and nk total pixels with gray level rk, is a histogram in gray level range [0, L – 1]. Then how can we normalize a histogram?

If each value of histogram is added by total number of pixels in image, say n, p(rk)=nk+n

If each value of histogram is subtracted by total number of pixels in image, say n, p(rk)=nk-n

If each value of histogram is multiplied by total number of pixels in image, say n, p(rk)=nk * n

If each value of histogram is divided by total number of pixels in image, say n, p(rk)=nk / n

What is the name of the phenomenon that corrupts the sampled image, and how does it happen?

Aliasing, if the band-limited functions are oversampled

Shannon sampling, if the band-limited functions are undersampled

Shannon sampling, if the band-limited functions are oversampled

Aliasing, if the band-limited functions are undersampled

Aliasing, if the band-limited functions are oversampled

43 + Mcqs in Image Enhancement in Digital Image Processing Page 1 McqOptions

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
C.	May be
D.	Can't Say
Answer» B. False

Discussion

2.	Assuming that the origin of F(u, v), Fourier transformed function of f(x, y) an input image, has been correlated by performing the operation f(x, y)(-1)x+y prior to taking the transform of the image. If F and f are of same size M*N, where does the point (u, v) =(0,0) shifts?
A.	(M -1, N -1)
B.	(M/2, N/2)
C.	(M+1, N+1)
D.	(0, 0)
Answer» C. (M+1, N+1)

Discussion

3.	Which of the following gives an expression for high boost filtered image fhb, if f represents an image, f blurred version of f, 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» E.

Discussion

4.	If r be the gray-level of image before processing and s after processing then which expression defines the power-law transformation, for the gray-level in the range [0, L-1]?
A.	s = L – 1 – r
B.	s = crᵞ, c and ᵞ are positive constants
C.	s = c log (1 + r), c is a constant and r ≥ 0
D.	none of the mentioned
Answer» C. s = c log (1 + r), c is a constant and r ≥ 0

Discussion

5.	A typical Fourier Spectrum with spectrum value ranging from 0 to 106, which of the following transformation is better to apply.
A.	Log transformations
B.	Power-law transformations
C.	Negative transformations
D.	None of the mentioned
Answer» B. Power-law transformations

Discussion

6.	The power-law transformation is given as: s = crᵞ, c and ᵞ are positive constants, and r is the gray-level of image before processing and s after processing. What happens if we increase the gamma value from 0.3 to 0.7?
A.	The contrast increases and the detail increases
B.	The contrast decreases and the detail decreases
C.	The contrast increases and the detail decreases
D.	The contrast decreases and the detail increases
Answer» D. The contrast decreases and the detail increases

Discussion

7.	What is standard deviation value for constant area?
A.	0
B.	1
C.	-1
D.	None of the mentioned
Answer» B. 1

Discussion

8.	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
Answer» D. All of the mentioned

Discussion

9.	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
Answer» D. None of the mentioned

Discussion

10.	A pixel p at coordinates (x, y) has neighbors whose coordinates are given by:(x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1, y-1)This set of pixels is called ____________
A.	4-neighbors of p
B.	Diagonal neighbors
C.	8-neighbors
D.	None of the mentioned
Answer» C. 8-neighbors

Discussion

11.	A pixel p at coordinates (x, y) has neighbors whose coordinates are given by:(x+1, y), (x-1, y), (x, y+1), (x, y-1)This set of pixels is called ____________
A.	4-neighbors of p
B.	Diagonal neighbors
C.	8-neighbors
D.	None of the mentioned
Answer» B. Diagonal neighbors

Discussion

12.	A bright image will have what kind of histogram, when the histogram, h(rk) = nk, rk the kth gray level and nk total pixels with gray level rk, is plotted nk versus rk?
A.	The histogram that are concentrated on the dark side of gray scale
B.	The histogram whose component are biased toward high side of gray scale
C.	The histogram that is narrow and centered toward the middle of gray scale
D.	The histogram that covers wide range of gray scale and the distribution of pixel is approximately uniform
Answer» C. The histogram that is narrow and centered toward the middle of gray scale

Discussion

13.	A low contrast image will have what kind of histogram when, the histogram, h(rk) = nk, rk the kth gray level and nk total pixels with gray level rk, is plotted nk versus rk?
A.	The histogram that are concentrated on the dark side of gray scale
B.	The histogram whose component are biased toward high side of gray scale
C.	The histogram that is narrow and centered toward the middle of gray scale
D.	The histogram that covers wide range of gray scale and the distribution of pixel is approximately uniform
Answer» D. The histogram that covers wide range of gray scale and the distribution of pixel is approximately uniform

Discussion

14.	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
Answer» E.

Discussion

15.	Which of the following fact is true for the masks that includes diagonal neighbors than the masks that doesn’t?
A.	Mask that excludes diagonal neighbors has more sharpness than the masks that doesn’t
B.	Mask that includes diagonal neighbors has more sharpness than the masks that doesn’t
C.	Both masks have same sharpness result
D.	None of the mentioned
Answer» C. Both masks have same sharpness result

Discussion

16.	The power-law transformation is given as: s = crᵞ, c and ᵞ are positive constants, and r is the gray-level of image before processing and s after processing. Then, for what value of c and ᵞ does power-law transformation becomes identity transformation?
A.	c = 1 and ᵞ < 1
B.	c = 1 and ᵞ > 1
C.	c = -1 and ᵞ = 0
D.	c = ᵞ = 1
Answer» E.

Discussion

17.	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 degree
B.	0 degree
C.	45 degree
D.	None of the mentioned
Answer» B. 0 degree

Discussion

18.	Using gray-level transformation, the basic function power-law deals with which of the following transformation?
A.	log and inverse-log transformations
B.	negative and identity transformations
C.	nth and nth root transformations
D.	all of the mentioned
Answer» C. nth and nth root transformations

Discussion

19.	If we use a Laplacian to obtain sharp image for unsharp mask filtered image fs(x, y) of f(x, y) as 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» B. fhb = A f(x, y) + ∇^2 f(x,y)

Discussion

20.	How aliasing does corrupts the sampled image?
A.	By introducing additional frequency components to the sampled function
B.	By removing some frequency components from the sampled function
C.	All of the mentioned
D.	None of the mentioned
Answer» B. By removing some frequency components from the sampled function

Discussion

21.	How can one reduce the aliasing effect on an image?
A.	By reducing the high-frequency components of image by blurring the image
B.	By increasing the high-frequency components of image by blurring the image
C.	By reducing the high-frequency components of image by clarifying the image
D.	By increasing the high-frequency components of image by clarifying the image
Answer» B. By increasing the high-frequency components of image by blurring the image

Discussion

22.	While Zooming, In order to perform gray-level assignment for any point in the overlay, we assign its gray level to the new pixel in the grid its closest pixel in the original image. What’s this method of gray-level assignment called?
A.	Neighbor Duplication
B.	Duplication
C.	Nearest neighbor Interpolation
D.	None of the mentioned
Answer» D. None of the mentioned

Discussion

23.	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 degree
B.	0 degree
C.	45 degree
D.	None of the mentioned
Answer» B. 0 degree

Discussion

24.	Why is scaling of Laplacian filtered images necessary?
A.	Because it contain high positive values
B.	Because it contain high negative value
C.	Because it contain both positive and negative values
D.	None of the mentioned
Answer» D. None of the mentioned

Discussion

25.	An enhanced image can be obtained as: g(x,y)=f(x,y)-∇^2 f(x,y), where Laplacian is being subtracted from f(x, y) the input image. What does this conclude?
A.	That the center spike would be negative
B.	That the immediate neighbors of center spike would be positive.
C.	All of the mentioned
D.	None of the mentioned
Answer» D. None of the mentioned

Discussion

26.	The transformation s = T(r) producing a gray level s for each pixel value r of input image. Then, if the T(r) is single valued 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
Answer» B. It is needed to restrict producing of some inverted gray levels in output

Discussion

27.	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
Answer» D. None of the mentioned

Discussion

28.	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
Answer» B. G must be strictly decreasing

Discussion

29.	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 limits. What is/are those limits?
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
Answer» D. All of the mentioned

Discussion

30.	What is the full form of CDF?
A.	Cumulative density function
B.	Contour derived function
C.	Cumulative distribution function
D.	None of the mentioned
Answer» D. None of the mentioned

Discussion

31.	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» B. Increase in K, increases the noise of pixel values

Discussion

32.	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
Answer» E.

Discussion

33.	The domain that refers to image plane itself and the domain that refers to Fourier transform of an image is/are :
A.	Spatial domain in both
B.	Frequency domain in both
C.	Spatial domain and Frequency domain respectively
D.	Frequency domain and Spatial domain respectively
Answer» D. Frequency domain and Spatial domain respectively

Discussion

34.	A mask of size 3*3 is formed using Laplacian including diagonal neighbors that has central coefficient as 9. Then, what would be the central coefficient of same mask if it is made without diagonal neighbors?
A.	5
B.	-5
C.	8
D.	-8
Answer» B. -5

Discussion

35.	The transformation s = T(r) producing a gray level s for each pixel value r of input image. Then, if the T(r) is monotonically increasing 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
Answer» C. It guarantees that the output gray level and the input gray level will be in same range

Discussion

36.	If h(rk) = nk, rk the kth gray level and nk total pixels with gray level rk, is a histogram in gray level range [0, L – 1]. Then how can we normalize a histogram?
A.	If each value of histogram is added by total number of pixels in image, say n, p(rk)=nk+n
B.	If each value of histogram is subtracted by total number of pixels in image, say n, p(rk)=nk-n
C.	If each value of histogram is multiplied by total number of pixels in image, say n, p(rk)=nk * n
D.	If each value of histogram is divided by total number of pixels in image, say n, p(rk)=nk / n
Answer» E.

Discussion

37.	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
Answer» D. None of the mentioned

Discussion

38.	State for the validation of the statement:“In general-purpose for a digital image of zooming and shrinking, where Bilinear Interpolation generally is the method of choice over nearest neighbor Interpolation”.
A.	True
B.	False
C.	May be
D.	Can't Say
Answer» B. False

Discussion

39.	Image Shrinking has an undesirable feature, that is ____________
A.	Aliasing effect
B.	False contouring effect
C.	Ridging effect
D.	Checkerboard effect
Answer» B. False contouring effect

Discussion

40.	Which of the following mask(s) is/are used to sharpen images by subtracting a blurred version of original image from the original image itself?
A.	Unsharp mask
B.	High-boost filter
C.	All of the mentioned
D.	None of the mentioned
Answer» D. None of the mentioned

Discussion

41.	The principal factor to determine the spatial resolution of an image is _______
A.	Quantization
B.	Sampling
C.	Contrast
D.	Dynamic range
Answer» C. Contrast

Discussion

42.	What is the name of the phenomenon that corrupts the sampled image, and how does it happen?
A.	Shannon sampling, if the band-limited functions are undersampled
B.	Shannon sampling, if the band-limited functions are oversampled
C.	Aliasing, if the band-limited functions are undersampled
D.	Aliasing, if the band-limited functions are oversampled
Answer» D. Aliasing, if the band-limited functions are oversampled

Discussion

43.	For a band-limited function, which Theorem says that “if the function is sampled at a rate equal to or greater than twice its highest frequency, the original function can be recovered from its samples”?
A.	Band-limitation theorem
B.	Aliasing frequency theorem
C.	Shannon sampling theorem
D.	None of the mentioned
Answer» D. None of the mentioned

Discussion

Explore topic-wise MCQs in Digital Image Processing.

“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?

Assuming that the origin of F(u, v), Fourier transformed function of f(x, y) an input image, has been correlated by performing the operation f(x, y)(-1)x+y prior to taking the transform of the image. If F and f are of same size M*N, where does the point (u, v) =(0,0) shifts?

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

If r be the gray-level of image before processing and s after processing then which expression defines the power-law transformation, for the gray-level in the range [0, L-1]?

A typical Fourier Spectrum with spectrum value ranging from 0 to 106, which of the following transformation is better to apply.

The power-law transformation is given as: s = crᵞ, c and ᵞ are positive constants, and r is the gray-level of image before processing and s after processing. What happens if we increase the gamma value from 0.3 to 0.7?

What is standard deviation value for constant area?

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?

The technique of Enhancement that has a specified Histogram processed image as result, is called?

A pixel p at coordinates (x, y) has neighbors whose coordinates are given by:(x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1, y-1)This set of pixels is called ____________

A pixel p at coordinates (x, y) has neighbors whose coordinates are given by:(x+1, y), (x-1, y), (x, y+1), (x, y-1)This set of pixels is called ____________

A bright image will have what kind of histogram, when the histogram, h(rk) = nk, rk the kth gray level and nk total pixels with gray level rk, is plotted nk versus rk?

A low contrast image will have what kind of histogram when, the histogram, h(rk) = nk, rk the kth gray level and nk total pixels with gray level rk, is plotted nk versus rk?

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?

Which of the following fact is true for the masks that includes diagonal neighbors than the masks that doesn’t?

The power-law transformation is given as: s = crᵞ, c and ᵞ are positive constants, and r is the gray-level of image before processing and s after processing. Then, for what value of c and ᵞ does power-law transformation becomes identity transformation?

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?

Using gray-level transformation, the basic function power-law deals with which of the following transformation?

If we use a Laplacian to obtain sharp image for unsharp mask filtered image fs(x, y) of f(x, y) as 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?

How aliasing does corrupts the sampled image?

How can one reduce the aliasing effect on an image?

While Zooming, In order to perform gray-level assignment for any point in the overlay, we assign its gray level to the new pixel in the grid its closest pixel in the original image. What’s this method of gray-level assignment called?

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?

Why is scaling of Laplacian filtered images necessary?

An enhanced image can be obtained as: g(x,y)=f(x,y)-∇^2 f(x,y), where Laplacian is being subtracted from f(x, y) the input image. What does this conclude?

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

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 __________

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?

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 limits. What is/are those limits?

What is the full form of CDF?

What is the full form for PDF, a fundamental descriptor of random variables i.e. gray values in an image?

The domain that refers to image plane itself and the domain that refers to Fourier transform of an image is/are :

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

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

If h(rk) = nk, rk the kth gray level and nk total pixels with gray level rk, is a histogram in gray level range [0, L – 1]. Then how can we normalize a histogram?

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 _______

State for the validation of the statement:“In general-purpose for a digital image of zooming and shrinking, where Bilinear Interpolation generally is the method of choice over nearest neighbor Interpolation”.

Image Shrinking has an undesirable feature, that is ____________

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

The principal factor to determine the spatial resolution of an image is _______

What is the name of the phenomenon that corrupts the sampled image, and how does it happen?

For a band-limited function, which Theorem says that “if the function is sampled at a rate equal to or greater than twice its highest frequency, the original function can be recovered from its samples”?