6.2 Image Enhancement and Restoration

Image enhancement and restoration are key techniques in biomedical imaging. They improve image quality, making it easier to spot important details. From tweaking contrast to removing noise, these methods help medical professionals see clearer, more accurate images.

These techniques use both spatial and frequency domain approaches. They can sharpen edges, reduce blur, and even out lighting. Understanding these methods is crucial for anyone working with medical images, as they directly impact diagnosis and treatment planning.

Image Enhancement Techniques

Spatial Domain Techniques and Histogram Manipulation

• Spatial domain techniques operate directly on image pixels
• Process involves modifying pixel values based on surrounding neighborhood
• Includes point processing methods alter individual pixel intensities
• Neighborhood processing methods consider groups of adjacent pixels
• Histogram equalization redistributes pixel intensities to enhance overall contrast
  • Spreads out the most frequent intensity values
  • Results in higher contrast images with a wider range of grayscale values
• Contrast stretching expands the range of intensity levels to span a desired range
  • Improves image contrast by stretching the range of intensity values
  • Maps the input intensity range to a wider output range

Frequency Domain Techniques and Image Sharpening

• Frequency domain techniques manipulate the image's Fourier transform
• Process involves converting the image to frequency domain, modifying frequencies, then converting back
• Low-pass filters attenuate high-frequency components (smoothing)
• High-pass filters amplify high-frequency components (edge enhancement)
• Sharpening enhances edges and fine details in an image
  • Achieved by emphasizing high-frequency components
  • Unsharp masking technique creates a blurred negative image to add to the original
  • Laplacian operator detects edges in all directions for sharpening

Image Restoration Methods

Noise Reduction and Deblurring

• Noise reduction aims to remove unwanted variations in image intensity
• Common noise types include Gaussian noise (uniform across the image) and salt-and-pepper noise (random white and black pixels)
• Median filtering effectively removes salt-and-pepper noise
  • Replaces each pixel with the median value of its neighboring pixels
• Gaussian filtering reduces Gaussian noise
  • Convolves the image with a Gaussian kernel to smooth out variations
• Deblurring reverses image degradation caused by motion or out-of-focus blur
• Inverse filtering attempts to reverse the blurring process in the frequency domain
  • Can amplify noise, making it impractical for many real-world applications

Advanced Filtering Techniques

• Wiener filter optimally balances noise reduction and image restoration
  • Minimizes the mean square error between the estimated and true image
  • Adapts to the local image variance, preserving edges better than linear filters
• Adaptive filtering adjusts its behavior based on local image statistics
  • Useful for images with spatially varying noise characteristics
• Non-local means filtering exploits image self-similarity for noise reduction
  • Averages pixel values from similar patches across the entire image
• Anisotropic diffusion filtering smooths images while preserving edges
  • Applies diffusion more strongly in homogeneous areas and less near edges

Image Processing Fundamentals

Fourier Transform and Convolution

• Fourier transform decomposes an image into its sinusoidal components
  • Converts spatial domain information to frequency domain
  • Enables efficient filtering and analysis of image frequencies
• Discrete Fourier Transform (DFT) used for digital images
  • Fast Fourier Transform (FFT) algorithm efficiently computes the DFT
• Convolution fundamental operation in image processing
  • Combines two functions to produce a third function
  • In image processing, often used to apply filters by convolving image with a kernel
• Convolution theorem states convolution in spatial domain equals multiplication in frequency domain
  • Enables efficient implementation of certain filters in the frequency domain

Edge Detection and Image Interpolation

• Edge detection identifies boundaries of objects within images
• Common edge detection methods:
  • Sobel operator computes image gradient in horizontal and vertical directions
  • Canny edge detector uses multi-stage algorithm for robust edge detection
    • Applies Gaussian filter, computes gradient magnitude and direction, performs non-maximum suppression, and hysteresis thresholding
• Image interpolation estimates pixel values at non-integer coordinates
• Needed for image resizing, rotation, and geometric transformations
• Common interpolation methods:
  • Nearest neighbor interpolation assigns value of closest pixel (fast but low quality)
  • Bilinear interpolation uses weighted average of four nearest pixels (better quality)
  • Bicubic interpolation considers 16 nearest pixels (higher quality but more computationally intensive)