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Color Image Enhancement — MCQs | Digital Image Processing

1. Who typically benefits most from color image enhancement in medical imaging applications?

(A) Patients with motion disorders


(B) Radiologists analyzing MRI scans


(C) Nurses recording patient vitals


(D) Surgeons performing open-heart surgery



2. Which of the following is a goal of color image enhancement?

(A) Increasing data compression


(B) Reducing image file size


(C) Improving visual perception of features


(D) Reconstructing lost image parts



3. What does histogram equalization in color images aim to enhance?

(A) File format compatibility


(B) Brightness and contrast


(C) Image rotation


(D) Compression ratio



4. Which component is typically processed separately in HSI-based color enhancement?

(A) Saturation


(B) Hue


(C) Intensity


(D) Alpha



5. Which color model allows more intuitive manipulation of color enhancement for human vision?

(A) RGB


(B) CMYK


(C) YUV


(D) HSI



6. Which of the following is a nonlinear technique for color enhancement?

(A) Bit-plane slicing


(B) Histogram equalization


(C) Logarithmic transformation


(D) Averaging filter



7. In histogram equalization of color images, which approach helps prevent color distortion?

(A) Apply equalization on RGB channels separately


(B) Convert to HSI and enhance intensity only


(C) Enhance hue and saturation directly


(D) Use lossy compression before enhancement



8. Which of the following is a common problem when enhancing color images using the RGB model directly?

(A) Loss of metadata


(B) Color distortion


(C) Alpha blending errors


(D) Noise underfitting



9. What is the main advantage of working in the HSI space for color enhancement?

(A) Smaller file size


(B) Channel independence


(C) Uniform spatial resolution


(D) Eliminates chromatic aberration



10. Which transform is often applied before enhancement to improve computational efficiency?

(A) Walsh transform


(B) Discrete Fourier transform


(C) RGB to HSI transform


(D) Laplace transform



11. Which of the following is often used to enhance underexposed regions in color images?

(A) Low-pass filtering


(B) Histogram clipping


(C) Adaptive histogram equalization


(D) Binary thresholding



12. Which operation is typically used for global contrast enhancement in color images?

(A) Convolution


(B) Gamma correction


(C) Morphological closing


(D) Fourier transform



13. Which domain is most commonly used in color image enhancement for feature highlighting?

(A) Spatial domain


(B) Wavelet domain


(C) Frequency domain


(D) Compressed domain



14. What is a primary goal of contrast stretching in color image enhancement?

(A) Reducing image resolution


(B) Increasing image depth


(C) Expanding the dynamic range


(D) Applying color quantization



15. Which of the following is typically true in color image enhancement techniques?

(A) Hue is enhanced independently of intensity


(B) Intensity and saturation are always processed together


(C) RGB model is used for contrast enhancement


(D) Gamma correction cannot be used on color images



16. Which component in YCbCr is most related to brightness?

(A) Y


(B) Cb


(C) Cr


(D) All equally



17. Why is YCbCr color space useful in image enhancement?

(A) It separates luminance from chrominance


(B) It compresses images automatically


(C) It works only with grayscale images


(D) It improves audio clarity



18. Which technique enhances local contrast in specific regions of a color image?

(A) Global histogram equalization


(B) Local histogram equalization


(C) RGB-to-CMYK conversion


(D) Inverse filtering



19. What does saturation control in an image?

(A) Sharpness


(B) Brightness


(C) Vividness of color


(D) Resolution



20. In color image enhancement, what can result from independently equalizing RGB channels?

(A) Better segmentation


(B) Improved color fidelity


(C) Artificial color shifts


(D) Lower computational load



21. Which technique is used to prevent noise amplification in enhanced color images?

(A) Edge detection


(B) Smoothing filter before enhancement


(C) Median thresholding


(D) Vector quantization



22. What does gamma correction mainly affect in an image?

(A) Noise


(B) Resolution


(C) Brightness levels


(D) Sharpness



23. What type of filtering helps improve low contrast in color images?

(A) Band-reject filtering


(B) High-pass filtering


(C) Low-pass filtering


(D) Homomorphic filtering



24. Which method ensures better color preservation during enhancement?

(A) Enhance RGB separately


(B) Work in YCbCr or HSI color space


(C) Apply Fourier transform


(D) Increase pixel size



25. Which of the following is a key step before enhancing satellite images in color?

(A) Convert to grayscale


(B) Register multispectral bands


(C) Rotate the image


(D) Decrease brightness



26. Which type of enhancement is preferred for real-time color video?

(A) Local enhancement


(B) Spatial convolution


(C) Global transformation


(D) Fast adaptive enhancement



27. Which component in the HSV model helps control how “pure” a color appears?

(A) Value


(B) Saturation


(C) Hue


(D) Gamma



28. What is the effect of applying a logarithmic transform on a color image?

(A) Increases low pixel values


(B) Sharpen edges


(C) Equalize histogram


(D) Boost hue saturation



29. Which model is commonly used in television broadcasting and is suitable for enhancement?

(A) RGB


(B) HSI


(C) YCbCr


(D) CMY



30. What does chrominance represent in a color image?

(A) Spatial resolution


(B) Intensity


(C) Hue and saturation information


(D) Gamma index



31. Why is histogram equalization not always suitable for color images?

(A) It reduces image contrast


(B) It causes spatial aliasing


(C) It may alter the perceived color


(D) It destroys chromaticity



32. Which function maps pixel values non-linearly in image enhancement?

(A) Averaging


(B) Linear scaling


(C) Gamma correction


(D) Bit-plane slicing



33. Which space is typically used for face enhancement and detection?

(A) CMYK


(B) HSV


(C) RGB


(D) YCbCr



34. Which color enhancement technique is used for improving underwater images?

(A) Color quantization


(B) White balancing


(C) Pseudo-coloring


(D) Region growing



35. What is one disadvantage of enhancing images in the RGB model?

(A) High storage requirement


(B) Hue shifts and distortion


(C) Limited dynamic range


(D) Fixed histogram bins



36. Which color enhancement technique adjusts color balance in shadows and highlights?

(A) Global contrast stretching


(B) Color grading


(C) Unsharp masking


(D) Fourier enhancement



37. Which color enhancement is often used in artistic and cinematic applications?

(A) Histogram matching


(B) Gamma equalization


(C) Color grading


(D) Median enhancement



38. What does CLAHE stand for in image enhancement?

(A) Color-Level Adaptive Histogram Equalization


(B) Contrast-Limited Adaptive Histogram Equalization


(C) Chromatic Linear Adaptive Histogram Equalization


(D) Contrast-Limited Averaged Histogram Expansion



39. Which enhancement technique prevents over-enhancement in uniform areas?

(A) CLAHE


(B) Global equalization


(C) Log transform


(D) High-boost filtering



40. What happens if color enhancement is too aggressive?

(A) Better image clarity


(B) Balanced colors


(C) Over-saturation and unnatural appearance


(D) Lower brightness



41. Which of the following is used to enhance shadows without affecting highlights?

(A) Histogram expansion


(B) Gamma compression


(C) Shadow masking


(D) Tone mapping



42. Why is tone mapping used in color image enhancement?

(A) For real-time data encryption


(B) To convert HDR images for display


(C) To extract edge information


(D) To segment color channels



43. What is the main function of color correction in image enhancement?

(A) Add noise


(B) Improve alignment


(C) Restore natural appearance


(D) Reduce bandwidth



44. Which of the following helps achieve color constancy in images?

(A) Median filtering


(B) White balance adjustment


(C) Bit-plane extraction


(D) Histogram smoothing



45. What does dynamic range compression aim to achieve?

(A) Remove high-frequency content


(B) Expand shadows


(C) Compress pixel intensity range


(D) Reduce frame rate



46. Which color model is generally not perceptually uniform?

(A) RGB


(B) Lab


(C) HSI


(D) YUV



47. Which space is best suited for perceptual uniformity in enhancement?

(A) RGB


(B) Lab


(C) CMY


(D) XYZ



48. What is the main advantage of working with Lab color space in enhancement tasks?

(A) Computational efficiency


(B) Human perception alignment


(C) Image segmentation simplicity


(D) High compression ratio



49. Which tool is used to adjust brightness, contrast, and color balance in one interface?

(A) Gamma curve


(B) Histogram


(C) Levels adjustment


(D) Bit slicing tool



50. Which enhancement strategy helps balance local and global contrast?

(A) CLAHE


(B) Fourier smoothing


(C) Log compression


(D) Binary thresholding



More MCQs on Digital image Processing

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  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

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  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

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  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

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  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

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