# Image Processing MCQ | Combining Spatial Enhancements Methods

500+ MCQ’s Questions of digital Image Processing mcq question 2021 – Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image.

Here’s the list of chapters on the “Digital Image Processing” subject covering 100+ topics. You can practice the MCQs chapter by chapter . digital image processing mcq questions

## DIP ALL CHAPTERS

1. Basic of Digital Image Processing
2. Digital Image Fundamentals
3. Intensity Transformations and Spatial Filtering
4. Filtering in Frequency Domain
5. Image Restoration and Reconstruction
6. Color Image Processing
7. Image Compression
8. Morphological Image Processing
9. Image Segmentation
10. Representation and Description
11. Wavelet based Image Processing
12. Image Enhancement
13. Object Recognition

## Combining Spatial Enhancements Methods

1. Which of the following make an image difficult to enhance?
a) Narrow range of intensity levels
b) Dynamic range of intensity levels
c) High noise
d) All of the mentioned

Explanation: All the mentioned options make it difficult to enhance an image.

1. Which of the following is a second-order derivative operator?
a) Histogram
b) Laplacian
c) Gaussian
d) None of the mentioned

Explanation: Laplacian is a second-order derivative operator.

1. Response of the gradient to noise and fine detail is _ the Laplacian’s.
a) equal to
b) lower than
c) greater than
d) has no relation with

Explanation: Response of the gradient to noise and fine detail is lower than the Laplacian’s and can further be lowered by smoothing.

1. Dark characteristics in an image are better solved using _
a) Laplacian Transform
b) Gaussian Transform
c) Histogram Specification
d) Power-law Transformation

Explanation: It can be solved by Histogram Specification but it is better handled by Power-law Transformation.

1. What is the smallest possible value of a gradient image?
a) e
b) 1
c) 0
d) -e

Explanation: The smallest possible value of a gradient image is 0.

1. Which of the following fails to work on dark intensity distributions?
a) Laplacian Transform
b) Gaussian Transform
c) Histogram Equalization
d) Power-law Transformation

Explanation: Histogram Equalization fails to work on dark intensity distributions.

1. _ is used to detect diseases such as bone infection and tumors.
a) MRI Scan
b) PET Scan
c) Nuclear Whole Body Scan
d) X-Ray

Explanation: Nuclear Whole Body Scan is used to detect diseases such as bone infection and tumors

1. How do you bring out more of the skeletal detail from a Nuclear Whole Body Bone Scan?
a) Sharpening
b) Enhancing
c) Transformation
d) None of the mentioned

Explanation: Sharpening is used to bring out more of the skeletal detail.

1. An alternate approach to median filtering is __
b) Gaussian filter
c) Sharpening
d) Laplacian filter

Explanation: Using a mask, formed from the smoothed version of the gradient image, can be used for median filtering.

1. Final step of enhancement lies in _ of the sharpened image.
a) Increase range of contrast
b) Increase range of brightness
c) Increase dynamic range
d) None of the mentioned

Explanation: Increasing the dynamic range of the sharpened image is the final step in enhancement.

## Fundamentals of Spatial Filtering

1. What is accepting or rejecting certain frequency components called as?
a) Filtering
b) Eliminating
c) Slicing
d) None of the Mentioned

Explanation: Filtering is the process of accepting or rejecting certain frequency components.

1. A filter that passes low frequencies is _
a) Band pass filter
b) High pass filter
c) Low pass filter
d) None of the Mentioned

Explanation: Low pass filter passes low frequencies.

1. What is the process of moving a filter mask over the image and computing the sum of products at each location called as?
a) Convolution
b) Correlation
c) Linear spatial filtering
d) Non linear spatial filtering

Explanation: The process is called as Correlation.

1. The standard deviation controls _ of the bell (2-D Gaussian function of bell shape).
a) Size
b) Curve
c) Tightness
d) None of the Mentioned

Explanation: The standard deviation controls “tightness” of the bell.

1. What is required to generate an M X N linear spatial filter?
b) M+N coordinates
c) MN spatial coefficients
d) None of the Mentioned

Explanation: To generate an M X N linear spatial filter MN mask coefficients must be specified.

1. What is the difference between Convolution and Correlation?
a) Image is pre-rotated by 180 degree for Correlation
b) Image is pre-rotated by 180 degree for Convolution
c) Image is pre-rotated by 90 degree for Correlation
d) Image is pre-rotated by 90 degree for Convolution

Explanation: Convolution is the same as Correlation except that the image must be rotated by 180 degrees initially.

1. Convolution and Correlation are functions of _
a) Distance
b) Time
c) Intensity
d) Displacement

Explanation: Convolution and Correlation are functions of displacement.

1. The function that contains a single 1 with the rest being 0s is called __
a) Identity function
b) Inverse function
c) Discrete unit impulse
d) None of the Mentioned

Explanation: It is called Discrete unit impulse.

1. Which of the following involves Correlation?
a) Matching
b) Key-points
c) Blobs
d) None of the Mentioned.

Explanation: Correlation is applied in finding matches.

1. An example of a continuous function of two variables is __
b) Intensity function
c) Contrast stretching
d) Gaussian function

Explanation: Gaussian function has two variables and is an exponential continuous function.

## Histogram Processing – 1

1. What is the basis for numerous spatial domain processing techniques?
a) Transformations
b) Scaling
c) Histogram
d) None of the Mentioned

Explanation: Histogram is the basis for numerous spatial domain processing techniques.

1. In _ image we notice that the components of histogram are concentrated on the low side on intensity scale.
a) bright
b) dark
c) colourful
d) All of the Mentioned

Explanation: Only in dark images, we notice that the components of histogram are concentrated on the low side on intensity scale.

1. What is Histogram Equalisation also called as?
a) Histogram Matching
b) Image Enhancement
c) Histogram linearisation
d) None of the Mentioned

Explanation: Histogram Linearisation is also known as Histogram Equalisation.

1. What is Histogram Matching also called as?
a) Histogram Equalisation
b) Histogram Specification
c) Histogram linearisation
d) None of the Mentioned

Explanation: Histogram Specification is also known as Histogram Matching.

1. Histogram Equalisation is mainly used for ____
a) Image enhancement
b) Blurring
d) None of the Mentioned

Explanation: It is mainly used for Enhancement of usually dark images.

1. To reduce computation if one utilises non-overlapping regions, it usually produces __ effect.
a) Dimming
b) Blurred
c) Blocky
d) None of the Mentioned

Explanation: Utilising non-overlapping regions usually produces “Blocky” effect.

1. What does SEM stands for?
a) Scanning Electronic Machine
b) Self Electronic Machine
c) Scanning Electron Microscope
d) Scanning Electric Machine

Explanation: SEM stands for Scanning Electron Microscope.

1. The type of Histogram Processing in which pixels are modified based on the intensity distribution of the image is called ___.
a) Intensive
b) Local
c) Global
d) Random

Explanation: It is called Global Histogram Processing.

1. Which type of Histogram Processing is suited for minute detailed enhancements?
a) Intensive
b) Local
c) Global
d) Random

Explanation: Local Histogram Processing is used.

1. In uniform PDF, the expansion of PDF is ____
a) Portable Document Format
b) Post Derivation Function
c) Previously Derived Function
d) Probability Density Function

Explanation: PDF stands for Probability Density Function.

## Histogram Processing – 2

1. The histogram of a digital image with gray levels in the range [0, L-1] is represented by a discrete function:
a) h(r_k)=n_k
b) h(r_k )=n/n_k
c) p(r_k )=n_k
d) h(r_k )=n_k/n

Explanation: The histogram of a digital image with gray levels in the range [0, L-1] is a discrete function h(rk )=nk, where rk is the kth gray level and nkis the number of pixels in the image having gray level rk.

1. How is the expression represented for the normalized histogram?
a) p(r_k )=n_k
b) p(r_k )=n_k/n
c) p(r_k)=nn_k
d) p(r_k )=n/n_k

Explanation: It is common practice to normalize a histogram by dividing each of its values by the total number of pixels in the image, denoted by n. Thus, a normalized histogram is given by p(rk )=nk/n, for k=0,1,2…..L-1. Loosely speaking, p(rk ) gives an estimate of the probability of occurrence of gray-level rk. Note that the sum of all components of a normalized histogram is equal to 1.

1. Which of the following conditions does the T(r) must satisfy?
a) T(r) is double-valued and monotonically decreasing in the interval 0≤r≤1; and
0≤T(r)≤1 for 0≤r≤1
b) T(r) is double-valued and monotonically increasing in the interval 0≤r≤1; and
0≤T(r)≤1 for 0≤r≤1
c) T(r) is single-valued and monotonically decreasing in the interval 0≤r≤1; and
0≤T(r)≤1 for 0≤r≤1
d) T(r) is single-valued and monotonically increasing in the interval 0≤r≤1; and
0≤T(r)≤1 for 0≤r≤1

Explanation: For any r satisfying the aforementioned conditions, we focus attention on transformations of the form
s=T(r) For 0≤r≤1
That produces a level s for every pixel value r in the original image.
For reasons that will become obvious shortly, we assume that the transformation function T(r) satisfies the following conditions:
T(r) is single-valued and monotonically increasing in the interval 0≤r≤1; and
0≤T(r)≤1 for 0≤r≤1.

1. The inverse transformation from s back to r is denoted as:
a) s=T-1(r) for 0≤s≤1
b) r=T-1(s) for 0≤r≤1
c) r=T-1(s) for 0≤s≤1
d) r=T-1(s) for 0≥s≥1

Explanation: The inverse transformation from s back to r is denoted by:
r=T-1(s) for 0≤s≤1.

1. The probability density function p_s (s) of the transformed variable s can be obtained by using which of the following formula?
a) p_s (s)=p_r (r)|dr/ds|
b) p_s (s)=p_r (r)|ds/dr|
c) p_r (r)=p_s (s)|dr/ds|
d) p_s (s)=p_r (r)|dr/dr|

Explanation: The probability density function p_s (s) of the transformed variable s can be obtained using a basic formula: p_s (s)=p_r (r)|dr/ds|
Thus, the probability density function of the transformed variable, s, is determined by the gray-level PDF of the input image and by the chosen transformation function.

1. A transformation function of particular importance in image processing is represented in which of the following form?
a) s=T(r)=∫0 (2r)pr (ω)dω
b) s=T(r)=∫0 (r-1)pr (ω)dω
c) s=T(r)=∫0 (r/2)pr (ω)dω
d) s=T(r)=∫0 pr (ω)dω

Explanation: A transformation function of particular importance in image processing has the form: s=T(r)=∫0 r pr(ω)dw, where ω is a dummy variable of integration. The right side of is recognized as the cumulative distribution function (CDF) of random variable r.

1. Histogram equalization or Histogram linearization is represented by of the following equation:
a) sk =∑k j =1 nj/n k=0,1,2,……,L-1
b) sk =∑k j =0 nj/n k=0,1,2,……,L-1
c) sk =∑k j =0 n/nj k=0,1,2,……,L-1
d) sk =∑k j =n nj/n k=0,1,2,……,L-1

Explanation: A plot of pk_ (rk) versus r_k is called a histogram .The transformation (mapping) given in sk =∑k j =0)k nj/n k=0,1,2,……,L-1 is called histogram equalization or histogram linearization.

1. What is the method that is used to generate a processed image that have a specified histogram?
a) Histogram linearization
b) Histogram equalization
c) Histogram matching
d) Histogram processing

Explanation: In particular, it is useful sometimes to be able to specify the shape of the histogram that we wish the processed image to have. The method used to generate a processed image that has a specified histogram is called histogram matching or histogram specification.

1. Histograms are the basis for numerous spatial domain processing techniques.
a) True
b) False

Explanation: Histograms are the basis for numerous spatial domain processing techniques. Histogram manipulation can be used effectively for image enhancement.

1. In a dark image, the components of histogram are concentrated on which side of the grey scale?
a) High
b) Medium
c) Low
d) Evenly distributed

Explanation: We know that in the dark image, the components of histogram are concentrated mostly on the low i.e., dark side of the grey scale. Similarly, the components of histogram of the bright image are biased towards the high side of the grey scale.

Digital Image Processing MCQ | Basics Of Image Sampling & Quantization

Basic of Digital Image Processing

ALL unite digital Image Processing MCQ