1. : Histogram equalization primarily aims to
(A) blur the image
(B) sharpen the image
(C) improve contrast
(D) reduce noise
2. : In histogram equalization, the transformation function is based on
(A) mean of intensities
(B) cumulative distribution function
(C) standard deviation
(D) histogram slope
3. : Histogram specification is also known as
(A) histogram remapping
(B) histogram clipping
(C) histogram shifting
(D) histogram matching
4. : Which operation produces a uniform output histogram?
(A) specification
(B) equalization
(C) shifting
(D) smoothing
5. : A disadvantage of histogram equalization is that it can
(A) reduce brightness
(B) create unnatural look
(C) require more computation
(D) blur edges
6. : Histogram specification uses a specified histogram for
(A) source image only
(B) target image only
(C) both source and target images
(D) neither
7. : The mapping in histogram equalization is
(A) linear
(B) non‑linear
(C) constant
(D) sinusoidal
8. : Histogram equalization is most effective when input histogram is
(A) uniform
(B) narrow and clustered
(C) bi‑modal
(D) sparse and wide
9. : Histogram specification requires two steps: equalize source, then
(A) invert the CDF
(B) apply target CDF
(C) normalize intensities
(D) smooth the histogram
10. : In histogram equalization, if input image has a large number of identical intensities, output may have
(A) full dynamic range
(B) gaps
(C) duplicate intensities
(D) negative values
11. : Which term describes the desired histogram in specification?
(A) source histogram
(B) equalized histogram
(C) reference histogram
(D) smoothed histogram
12. : Histogram equalization does not require a reference image. True or False?
(A) True
(B) False
(C) Sometimes
(D) Not applicable
13. : For grayscale images with L levels, the equalization mapping is:
(A) sᵢ = (L‑1) * ∑ p(rⱼ)
(B) sᵢ = ∑ rᵢ
(C) sᵢ = L * rᵢ
(D) sᵢ = ∑ rᵢ / L
14. : Histogram specification can achieve a histogram that is
(A) arbitrary shape
(B) always Gaussian
(C) always uniform
(D) histogram equalization outcome only
15. : After histogram equalization, mean intensity typically shifts toward
(A) mid‑range
(B) lowest intensity
(C) highest intensity
(D) unchanged
16. : Histogram equalization is a
(A) global enhancement
(B) local enhancement
(C) edge detection
(D) frequency domain method
17. : A disadvantage of histogram specification is needing
(A) more memory
(B) a reference histogram
(C) color images only
(D) local operators
18. : Histogram equalization stretches the dynamic range by redistributing pixel intensities across
(A) one channel
(B) two channels
(C) available levels
(D) spatial domain only
19. : Equalization is less effective on images that already have
(A) low contrast
(B) uniform brightness
(C) narrow histograms
(D) high noise
20. : In histogram specification, mapping functions are derived from two CDFs: source and
(A) input
(B) output/reference
(C) mean
(D) difference
21. : Which process can preserve specific tonal characteristics?
(A) histogram equalization
(B) histogram specification
(C) convolution
(D) edge detection
22. : In equalization, the output histogram approximates
(A) Gaussian distribution
(B) Poisson distribution
(C) uniform distribution
(D) Laplacian distribution
23. : Which type of images may suffer over‑enhancement after equalization?
(A) underwater
(B) medical scans
(C) natural scenery
(D) portrait images
24. : Histogram specification can map a dark image to resemble
(A) same histogram as the source
(B) bright image histogram
(C) equalized version only
(D) smaller histogram
25. : The complexity of both equalization and specification algorithms is on the order of
(A) O(n log n)
(B) O(n²)
(C) O(n)
(D) O(nL)
26. : Histogram equalization is applied in
(A) spatial domain
(B) frequency domain
(C) wavelet domain
(D) gradient domain
27. : Histogram specification can better match
(A) average brightness
(B) contrast distribution
(C) texture details
(D) edge strength
28. : If the reference histogram is uniform, specification is equivalent to
(A) smoothing
(B) equalization
(C) thresholding
(D) normalization
29. : Mapping functions in specification may not be strictly monotonic if
(A) histograms are noisy
(B) reference distribution is discrete
(C) intensities repeat
(D) functions always monotonic
30. : Histogram equalization may amplify
(A) noise
(B) edges
(C) color fidelity
(D) segmentation
31. : In color images, histogram equalization is typically done on
(A) each RGB channel separately
(B) intensity channel only
(C) hue channel only
(D) saturation channel only
32. : Global histogram equalization can fail when image has varying
(A) illumination across regions
(B) uniform texture
(C) high resolution
(D) grayscale only
33. : A local version of histogram equalization is called
(A) adaptive histogram equalization (AHE)
(B) Fourier equalization
(C) gradient equalization
(D) CDF equalization
34. : Histogram specification is useful to standardize images from
(A) same camera only
(B) different sources
(C) low contrast only
(D) grayscale only
35. : Equalization mapping s = T(r) ensures T is
(A) discontinuous
(B) non‑decreasing
(C) decreasing
(D) constant
36. : Histogram equalization requires calculating
(A) spatial gradients
(B) intensity derivative
(C) cumulative histogram
(D) edge map
37. : In histogram specification, the inverse mapping is used to find
(A) source CDF values
(B) reference intensity for a given output
(C) gradient threshold
(D) image mean
38. : If two different reference histograms produce identical images under specification, they must be
(A) identical shapes
(B) complementary
(C) random
(D) scalar multiples
39. : Histogram equalization is best for images with
(A) high contrast detail
(B) flat brightness
(C) many saturated pixels
(D) high noise level
40. : Histogram specification cannot generate
(A) dark output from bright input
(B) histogram narrower than source
(C) specified output histogram
(D) mapping inversion
41. : Which method can preserve histogram shape?
(A) equalization
(B) specification
(C) smoothing
(D) thresholding
42. : In specification, if reference histogram is darker, output will be
(A) brighter
(B) darker
(C) unchanged
(D) inverted
43. : Histogram equalization tends to stretch regions with
(A) low slope in CDF
(B) high slope in histogram
(C) uniform probabilities
(D) zero frequencies
44. : Mapping table for equalization is often implemented via
(A) lookup table (LUT)
(B) convolution filter
(C) wavelet filter
(D) histogram smoothing
45. : Which method deals with user‑defined output histogram?
(A) histogram equalization
(B) histogram specification
(C) thresholding
(D) edge enhancement
46. : Histogram equalization will map the lowest input intensity to
(A) zero
(B) mid‑level
(C) highest level
(D) undefined
47. : After specification, the output histogram matches the reference ideally if
(A) both histograms continuous
(B) mapping exact inverse exists
(C) source has all gray‑levels
(D) operations global
48. : A limitation of histogram matching is poor results if the reference histogram is
(A) very smooth
(B) extremely peaked
(C) uniform
(D) flat
49. : Enhanced contrast after equalization is subjectively pleasing when
(A) image has correct exposure
(B) image underexposed or overexposed
(C) edges are blurred
(D) colors are synthetic
50. : Both histogram equalization and specification are
(A) spatial domain techniques
(B) frequency domain techniques
(C) color space conversions
(D) segmentation tools
