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

50
Score: 0
Attempted: 0/50
Subscribe
1. : What is the purpose of the negative transformation in image processing?



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



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



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



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



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



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



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



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



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



11. : Log transformations are effective in:



12. : Negative transformation is mostly used for:



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



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



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



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



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



18. : Negative image transformation flips:



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

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

23. : Which transformation is NOT non-linear?



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



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



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



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



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


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



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



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



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



33. : In a negative image, black becomes:



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


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



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



37. : Which transformation is linear among the following?



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


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



40. : Negative transformation is applied mainly on:



41. : Log transformation is not suitable for:



42. : A gamma value equal to 1 means:



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



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



45. : Gamma correction is most important in:



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



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

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



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



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

Related Posts:

Contents Copyrights Reserved By T4Tutorials