1. Who introduced the concept of adaptive filtering in image processing?
(A) Claude Shannon
(B) Alan Turing
(C) Widrow and Hoff
(D) Gonzalez and Woods
2. Which of the following best describes an adaptive filter?
(A) A fixed kernel filter
(B) A filter that adapts based on local image statistics
(C) A filter with constant coefficients
(D) A filter only for binary images
3. What is the main advantage of adaptive filters in image processing?
(A) Faster computation
(B) Uniform smoothing
(C) Noise reduction without blurring edges
(D) Increased image size
4. Which type of noise is best removed using adaptive filters?
(A) Salt and pepper noise
(B) Gaussian noise
(C) Speckle noise
(D) Periodic noise
5. Adaptive filters operate based on:
(A) Frequency domain parameters
(B) Neighborhood histograms
(C) Local mean and variance
(D) Global thresholding
6. Which of the following is an example of an adaptive filter?
(A) Mean filter
(B) Gaussian filter
(C) Wiener filter
(D) Laplacian filter
7. Wiener filter assumes the signal and noise are:
(A) Deterministic
(B) Binary
(C) Stationary random processes
(D) Linear functions
8. Which domain is commonly used in adaptive filtering?
(A) Spatial domain
(B) Frequency domain
(C) Time domain
(D) Histogram domain
9. What does an adaptive filter do when local variance is high?
(A) Applies more smoothing
(B) Applies less smoothing
(C) Enhances contrast
(D) Ignores the region
10. When is adaptive filtering most useful?
(A) For sharpening edges
(B) For removing uniform noise
(C) For images with varying noise levels
(D) For low-resolution images
11. What characteristic of a pixel neighborhood is typically analyzed in adaptive filtering?
(A) Shape
(B) Texture
(C) Intensity and variance
(D) Histogram bin count
12. The Wiener filter works optimally under which condition?
(A) Known noise and signal statistics
(B) Constant illumination
(C) Low-resolution images
(D) Fixed kernel size
13. Adaptive filters preserve:
(A) Global patterns
(B) Low-frequency details only
(C) Edges and fine structures
(D) Image size
14. Which parameter is dynamically adjusted in adaptive filtering?
(A) Filter size
(B) Filter shape
(C) Filter coefficients
(D) Image resolution
15. Which of the following is not a benefit of adaptive filtering?
(A) Edge preservation
(B) Context-aware smoothing
(C) Uniform blurring
(D) Noise reduction
16. What does the adaptive median filter use to change its window size?
(A) Global threshold
(B) Mean value
(C) Pixel intensity
(D) Local noise characteristics
17. Adaptive filtering differs from traditional filtering by:
(A) Operating on binary images only
(B) Ignoring local statistics
(C) Adapting to image content
(D) Using convolutional neural networks
18. Which filter is known to adapt to both spatial and frequency content?
(A) Butterworth filter
(B) Gabor filter
(C) Adaptive Wiener filter
(D) Gaussian filter
19. The adaptive Wiener filter minimizes:
(A) Mean Absolute Error
(B) Peak Signal to Noise Ratio
(C) Mean Square Error
(D) Edge contrast
20. Which image quality metric often improves after adaptive filtering?
(A) Brightness
(B) Contrast
(C) Signal-to-noise ratio
(D) Hue
21. In adaptive filtering, larger window size results in:
(A) Better edge preservation
(B) Higher blurring
(C) Lower noise detection
(D) Reduced contrast
22. The term “adaptive” in adaptive filtering refers to:
(A) Resolution independence
(B) Response to global image properties
(C) Adjustment to local image variations
(D) Predefined filter selection
23. Which of the following is typically not considered in adaptive filter design?
(A) Local mean
(B) Local variance
(C) Image histogram
(D) Global Fourier transform
24. What is the effect of adaptive filtering on image edges?
(A) Edges are blurred
(B) Edges are sharpened
(C) Edges are preserved
(D) Edges are removed
25. Adaptive filtering can be used in which application?
(A) JPEG compression
(B) Edge detection
(C) Noise estimation and removal
(D) Color quantization
26. What is a drawback of adaptive filters?
(A) Excessive blurring
(B) High computational cost
(C) Fixed kernel size
(D) Inability to remove Gaussian noise
27. Which component is essential for Wiener filter computation?
(A) Noise color
(B) Local variance
(C) Image entropy
(D) Histogram equalization
28. The adaptive filter performs well in regions with:
(A) Low contrast
(B) Constant color
(C) Homogeneous and heterogeneous textures
(D) Binary transitions
29. Which of the following is not true about adaptive median filters?
(A) They increase window size if needed
(B) They work well on salt and pepper noise
(C) They preserve fine details
(D) They assume Gaussian noise
30. What differentiates adaptive median filters from standard median filters?
(A) Use of mean values
(B) Variable window size
(C) Use of Fourier transform
(D) Convolution-based smoothing
31. Adaptive filters are considered:
(A) Linear filters
(B) Non-linear filters
(C) Hybrid filters
(D) Discrete filters
32. In adaptive filtering, when noise variance is low, the filter behaves like a:
(A) High-pass filter
(B) Low-pass filter
(C) Identity filter
(D) Gradient filter
33. What is the first step in most adaptive filtering processes?
(A) Fourier transform
(B) Local histogram analysis
(C) Estimation of local statistics
(D) Threshold segmentation
34. Which of the following is an adaptive filter used in time-varying systems?
(A) LMS filter
(B) Gaussian filter
(C) Sobel filter
(D) Prewitt filter
35. The LMS filter stands for:
(A) Least Mean Square
(B) Linear Mean Square
(C) Local Median Square
(D) Least Maximum Signal
36. Adaptive filters are more accurate than fixed filters when:
(A) Images are uniformly noisy
(B) Noise is spatially variant
(C) Edges are absent
(D) Only salt noise is present
37. What makes adaptive filters computationally intensive?
(A) Predefined filter parameters
(B) Frequency domain operations
(C) Real-time statistical calculations
(D) Color space conversion
38. In real-time applications, adaptive filters require:
(A) GPU acceleration
(B) Histogram equalization
(C) Threshold segmentation
(D) Morphological operations
39. Which filter works on the principle of minimizing the overall error in prediction?
(A) Laplacian filter
(B) LMS adaptive filter
(C) Gaussian filter
(D) High-boost filter
40. Which method is used to evaluate adaptive filter performance?
(A) PSNR
(B) Histogram comparison
(C) Image entropy
(D) RGB mean
41. Which signal processing concept supports adaptive filtering in images?
(A) Modulation
(B) Spectral subtraction
(C) System identification
(D) Histogram stretching
42. Which of the following techniques is often used with adaptive filtering for better results?
(A) Histogram equalization
(B) Edge detection
(C) Local noise estimation
(D) Binary thresholding
43. Which of the following is not usually modified in adaptive filtering?
(A) Image size
(B) Filter response
(C) Filter kernel
(D) Filter weights
44. Adaptive filters are widely used in:
(A) Synthetic image generation
(B) Audio denoising
(C) Cartoon rendering
(D) 3D rendering
45. In adaptive filtering, the filter response changes with:
(A) Brightness of the screen
(B) Local statistics
(C) Image resolution
(D) Global histogram
46. Which mathematical operation is central to LMS adaptive filters?
(A) Integration
(B) Differentiation
(C) Convolution
(D) Error minimization
47. An ideal adaptive filter would:
(A) Blur the entire image
(B) Remove noise while preserving structures
(C) Sharpen all features equally
(D) Ignore local changes
48. Adaptive filters are particularly useful in:
(A) Text compression
(B) Medical imaging
(C) Steganography
(D) Data encryption
49. Which factor is dynamically updated in an LMS adaptive filter?
(A) Kernel size
(B) Learning rate
(C) Histogram range
(D) Color depth
