Wavelet Denoising in Digital Image Processing

The following content presents wavelet denoising code for digital image processing. Wavelet denoising is a commonly used image processing technique that effectively reduces noise and enhances image quality. This implementation utilizes MATLAB, a popular mathematical software widely employed in various scientific and engineering fields. MATLAB offers powerful toolboxes and functions that facilitate comprehensive analysis and processing of digital images.

Code Implementation:

% Wavelet Denoising Algorithm % Read input image im = imread('image.jpg'); % Convert to grayscale using rgb2gray function im_gray = rgb2gray(im); % Add Gaussian noise with 0.02 variance using imnoise function im_noise = imnoise(im_gray, 'gaussian', 0.02); % Apply wavelet denoising using wdenoise2 function im_denoised = wdenoise2(im_noise); % Display denoised result using imshow imshow(im_denoised);

This implementation demonstrates a complete wavelet denoising workflow. The process begins by reading an input image and converting it to grayscale format. Gaussian noise is then artificially introduced to simulate real-world image degradation scenarios. The core denoising operation utilizes MATLAB's wdenoise2 function, which applies wavelet thresholding techniques to remove noise while preserving important image features. The algorithm works by decomposing the image into different frequency components using wavelet transforms, applying thresholding to remove noise coefficients, and reconstructing the denoised image. Finally, the processed image is displayed for visual assessment. This example provides practical insights into implementing wavelet-based denoising techniques using MATLAB's image processing capabilities.