Site icon T4Tutorials.com

Basic Image Operations (Negative, Log, Power-law) — MCQs | Digital Image Processing

1. : What is the purpose of the negative transformation in image processing?

(A) Enhance edges


(B) Highlight high-intensity values


(C) Invert image intensities


(D) Apply histogram equalization



2. : Which basic transformation is defined by the formula s = L – 1 – r?

(A) Log Transformation


(B) Negative Transformation


(C) Power-law Transformation


(D) Thresholding



3. : What does ‘r’ typically represent in image transformation functions?

(A) Filter value


(B) Pixel location


(C) Input intensity level


(D) Image size



4. : Which transformation is suitable for expanding the values of dark pixels in an image while compressing the brighter ones?

(A) Log Transformation


(B) Negative Transformation


(C) Gamma < 1 in Power-law


(D) Thresholding



5. : The log transformation function is generally expressed as:

(A) s = log(r + 1)


(B) s = L – r


(C) s = c * log(1 + r)


(D) s = r^γ



6. : What role does the constant ‘c’ play in the log and power-law transformations?

(A) Controls direction


(B) Adjusts image resolution


(C) Scales the output intensity


(D) Reduces contrast



7. : Power-law transformation is commonly referred to as:

(A) Negative scaling


(B) Gamma correction


(C) Bit slicing


(D) Masking



8. : Which of the following is used to correct image brightness in displays?

(A) Log transform


(B) Negative transform


(C) Gamma correction


(D) Laplacian filter



9. : If γ > 1 in power-law transformation, what effect is observed?

(A) Bright areas get darker


(B) Image is inverted


(C) Overall image gets lighter


(D) Pixels are blurred



10. : In power-law transformation, the formula is:

(A) s = c + log(r)


(B) s = c * r^γ


(C) s = 1 / (1 + r^2)


(D) s = r – c



11. : Log transformations are effective in:

(A) Enhancing background noise


(B) Displaying low intensity details


(C) Compressing shadows


(D) Equalizing histogram



12. : Negative transformation is mostly used for:

(A) Grayscale compression


(B) Medical imaging like x-rays


(C) Histogram equalization


(D) Reducing contrast



13. : Which transformation can enhance details in dark regions more than in bright regions?

(A) Negative


(B) Logarithmic


(C) Linear


(D) Thresholding



14. : When using gamma correction with γ < 1, the output image becomes:

(A) Darker


(B) Inverted


(C) Brighter


(D) No change



15. : Negative of a grayscale image in 8-bit representation is obtained by:

(A) Subtracting pixel value from 128


(B) Using r^2


(C) Subtracting from 255


(D) Using s = log(r)



16. : What kind of function is s = c * r^γ?

(A) Linear


(B) Exponential


(C) Power-law


(D) Logarithmic



17. : For an image with high dynamic range, which transformation is best suited?

(A) Power-law


(B) Thresholding


(C) Logarithmic


(D) Negative



18. : Negative image transformation flips:

(A) Image vertically


(B) Pixel values around midpoint


(C) Contrast histogram


(D) Image axes



19. : In power-law transformation, which value of gamma increases contrast in bright regions?

1′)” /> (A) 0.5


1′)” /> (B) 1


1″ onclick=”checkAnswer(‘q19’, ‘>1’)” /> (C) >1


1′)” /> (D) 0



20. : Gamma correction is essential in:

(A) Printing


(B) Image compression


(C) Display systems


(D) Audio processing



21. : Which transformation maps high input intensity values to low output intensities?

1′)” /> (A) Negative


1′)” /> (B) Linear


1′)” /> (C) Log


1″ onclick=”checkAnswer(‘q21’, ‘Power-law with γ > 1’)” /> (D) Power-law with γ > 1



22. : The logarithmic transformation compresses:

(A) Dark pixels


(B) All pixels equally


(C) Bright pixel values


(D) Histogram size



23. : Which transformation is NOT non-linear?

(A) Logarithmic


(B) Negative


(C) Power-law


(D) Histogram equalization



24. : In gamma correction, if the output is too bright, the likely cause is:

(A) γ < 1


1″ onclick=”checkAnswer(‘q24’, ‘γ < 1')" /> (B) γ > 1


(C) Missing constant


(D) Low-pass filtering



25. : Which transformation would convert a dark image to a brighter one using exponentiation?

(A) s = log(r + 1)


(B) s = 255 – r


(C) s = c * r^γ with γ < 1


(D) s = c * log(r)



26. : What is the effect of applying a negative transformation to an image with mostly bright areas?

(A) It becomes blurred


(B) It becomes darker


(C) It becomes sharper


(D) It remains unchanged



27. : The power-law transformation is particularly useful for correcting:

(A) Blurry images


(B) Noise


(C) Illumination problems


(D) Edge detection



28. : Which transformation emphasizes details in low-intensity pixel regions?

(A) Negative


(B) Log


1″ onclick=”checkAnswer(‘q28’, ‘Log’)” /> (C) Gamma > 1


(D) Inverse transform



29. : In 8-bit images, the range of pixel values is:

(A) 0–100


(B) 0–127


(C) 0–255


(D) 0–512



30. : Which transformation would you use to reverse the brightness levels of an image?

(A) Log


(B) Gamma


(C) Negative


(D) Threshold



31. : The primary purpose of log transformation is to:

(A) Invert image colors


(B) Suppress high intensity values


(C) Enhance edges


(D) Increase spatial resolution



32. : In power-law transformation, γ = 1 represents:

(A) Negative transform


(B) Linear transform


(C) Log transform


(D) Binary threshold



33. : In a negative image, black becomes:

(A) White


(B) Gray


(C) Unchanged


(D) Inverted



34. : Which transformation compresses the dynamic range of pixel values?

(A) Histogram Equalization


(B) Logarithmic


1″ onclick=”checkAnswer(‘q34’, ‘Logarithmic’)” /> (C) Power-law with γ > 1


(D) Negative



35. : The power-law transformation is also known as:

(A) Contrast inversion


(B) Log enhancement


(C) Gamma correction


(D) Histogram spreading



36. : A gamma value of 0.4 in power-law transform causes:

(A) No change


(B) Contrast to increase in bright regions


(C) Image darkening


(D) Image brightening



37. : Which transformation is linear among the following?

(A) Negative


(B) Gamma correction


(C) Log


(D) s = a * r + b



38. : Which transformation is best for compressing high-intensity values?

1″ onclick=”checkAnswer(‘q38’, ‘Logarithmic’)” /> (A) Power-law with γ > 1


(B) Negative


(C) Logarithmic


(D) Linear



39. : Power-law transformations can be used to model:

(A) Color inversion


(B) Human visual perception


(C) Noise filtering


(D) Spatial resolution



40. : Negative transformation is applied mainly on:

(A) Color images


(B) Binary images


(C) Grayscale images


(D) Noise images



41. : Log transformation is not suitable for:

(A) Displaying bright pixels


(B) Enhancing details in dark areas


(C) Compressing large intensity values


(D) Expanding low values



42. : A gamma value equal to 1 means:

(A) Log transform


(B) Negative


(C) No change


(D) Inversion



43. : In the log transformation function, the log base used is typically:

(A) 2


(B) 10


(C) e


(D) Irrelevant, as scaling constant manages it



44. : Which transformation can visually reverse an X-ray image?

(A) Logarithmic


(B) Negative


(C) Linear


(D) Threshold



45. : Gamma correction is most important in:

(A) Scanning


(B) Display monitors


(C) Compression


(D) Noise reduction



46. : What happens when you apply power-law transformation with γ < 1?

(A) The image darkens


(B) The image gets noisier


(C) The image brightens


(D) No change



47. : In power-law transformation, the exponent γ is:

1′)” /> (A) Always 1


1″ onclick=”checkAnswer(‘q47’, ‘Can be < 1 or > 1′)” /> (B) Can be < 1 or > 1


1′)” /> (C) Always < 1


1′)” /> (D) Used for thresholding



48. : Which transformation is useful for correcting underexposed images?

(A) Log


(B) Negative


(C) Power-law with γ < 1


(D) Histogram equalization



49. : Which transformation is NOT used to manipulate pixel intensity values?

(A) Negative


(B) Log


(C) Gamma


(D) Dilation



50. : Power-law transformation modifies pixel intensities based on:

(A) Their color


(B) Logarithmic scale


(C) An exponential function


(D) A power function



More MCQs on Digital image Processing

  1. Introduction to DIP — MCQs | Digital Image Processing

  2. Human Visual System (HVS) — MCQs | Digital Image Processing

  3. Image Acquisition Devices — MCQs | Digital Image Processing

  4. Image Sampling & Quantization — MCQs | Digital Image Processing

  5. Image Resolution & Bit Depth — MCQs | Digital Image Processing

  6. Basic Image Operations (Negative, Log, Power-law) — MCQs | Digital Image Processing

  7. Histogram Equalization & Specification — MCQs | Digital Image Processing

  8. Contrast Stretching — MCQs | Digital Image Processing

  9. Image Arithmetic (Add, Subtract, Multiply, Divide) — MCQs | Digital Image Processing

  10. Bit-plane Slicing — MCQs | Digital Image Processing

  11. Smoothing Filters (Mean, Gaussian, Median) — MCQs | Digital Image Processing

  12. Sharpening Filters (Laplacian, Gradient) — MCQs | Digital Image Processing

  13. High-Boost Filtering — MCQs | Digital Image Processing

  14. Edge Detection (Sobel, Prewitt, Roberts, Canny, LoG) — MCQs | Digital Image Processing

  15. Fourier Transform (DFT, FFT) — MCQs | Digital Image Processing

  16. Frequency Domain Filtering — MCQs | Digital Image Processing

  17. Low-pass & High-pass Filters — MCQs | Digital Image Processing

  18. Homomorphic Filtering — MCQs | Digital Image Processing

  19. Noise Models (Gaussian, Salt & Pepper, Speckle) — MCQs | Digital Image Processing

  20. Adaptive Filtering — MCQs | Digital Image Processing

  21. Inverse & Wiener Filtering — MCQs | Digital Image Processing

  22. Pseudo-color & True-color Processing — MCQs | Digital Image Processing

  23. Color Space Conversion (RGB ↔ HSV, HSI, YCbCr) — MCQs | Digital Image Processing

  24. Color Image Enhancement — MCQs | Digital Image Processing

  25. Image Segmentation (Thresholding, Otsu, K-means, Region Growing) — MCQs | Digital Image Processing

  26. Edge-based Segmentation — MCQs | Digital Image Processing

  27. Region Splitting and Merging — MCQs | Digital Image Processing

  28. Watershed Algorithm — MCQs | Digital Image Processing

  29. Morphological Operations (Erosion, Dilation, Opening, Closing) — MCQs | Digital Image Processing

  30. Boundary Extraction — MCQs | Digital Image Processing

  31. Skeletonization — MCQs | Digital Image Processing

  32. Connected Components Labeling — MCQs | Digital Image Processing

  33. Texture Analysis (GLCM, LBP, Gabor Filters) — MCQs | Digital Image Processing

  34. Shape Descriptors (Perimeter, Area, Compactness, Eccentricity) — MCQs | Digital Image Processing

  35. Statistical Features (Mean, Variance, Skewness) — MCQs | Digital Image Processing

  36. Principal Component Analysis (PCA) — MCQs | Digital Image Processing

  37. Linear Discriminant Analysis (LDA) — MCQs | Digital Image Processing

  38. Feature Matching (SIFT, SURF, ORB) — MCQs | Digital Image Processing

  39. Image Registration — MCQs | Digital Image Processing

  40. Image Stitching — MCQs | Digital Image Processing

  41. Motion Detection & Optical Flow — MCQs | Digital Image Processing

  42. Background Subtraction — MCQs | Digital Image Processing

  43. Object Detection & Tracking — MCQs | Digital Image Processing

  44. Template Matching — MCQs | Digital Image Processing

  45. Pattern Recognition (KNN, SVM, ANN) — MCQs | Digital Image Processing

  46. Image Classification — MCQs | Digital Image Processing

  47. Image Clustering — MCQs | Digital Image Processing

  48. Image Compression (RLE, Huffman, LZW, JPEG, JPEG2000) — MCQs | Digital Image Processing

  49. Video Compression (MPEG, H.264) — MCQs | Digital Image Processing

  50. Image Fusion (Pixel, Feature, Decision Level) — MCQs | Digital Image Processing

  51. Image Watermarking — MCQs | Digital Image Processing

  52. Steganography — MCQs | Digital Image Processing

  53. Face Detection & Recognition — MCQs | Digital Image Processing

  54. Gesture Recognition — MCQs | Digital Image Processing

  55. 3D Image Processing — MCQs | Digital Image Processing

  56. Stereo Vision & Depth Estimation — MCQs | Digital Image Processing

  57. Medical Image Analysis (CT, MRI, Ultrasound) — MCQs | Digital Image Processing

  58. Remote Sensing Image Processing — MCQs | Digital Image Processing

  59. Satellite Image Enhancement — MCQs | Digital Image Processing

  60. Deep Learning for Image Processing (CNN, GANs, Autoencoders) — MCQs | Digital Image Processing

  61. Image Captioning — MCQs | Digital Image Processing

  62. Semantic & Instance Segmentation (Mask R-CNN, U-Net) — MCQs | Digital Image Processing

  63. Super Resolution (SRCNN, ESRGAN) — MCQs | Digital Image Processing

  64. Image Inpainting — MCQs | Digital Image Processing

  65. Image Style Transfer — MCQs | Digital Image Processing

  66. Real-Time Image Processing — MCQs | Digital Image Processing

  67. Augmented Reality (AR) & Virtual Reality (VR) — MCQs | Digital Image Processing

  68. DIP using MATLAB/OpenCV/Python — MCQs | Digital Image Processing

  69. DIP in IoT & Embedded Systems — MCQs | Digital Image Processing

  70. Ethics & Privacy in Image Processing — MCQs | Digital Image Processing

Computer Science Repeated MCQs Book Download

Exit mobile version