Image Denoising with MATLAB

This guide demonstrates image noise addition and denoising implementation using MATLAB's Image Processing Toolbox. The process begins by loading and preprocessing images using functions like imread() for image input and im2double() for data normalization. For noise introduction, MATLAB provides built-in functions such as imnoise() which supports various noise models including Gaussian noise (with controllable mean and variance parameters) and salt-and-pepper noise (with adjustable density). The denoising phase employs multiple algorithmic approaches: median filtering using medfilt2() for impulse noise removal, mean filtering through fspecial() and imfilter() for Gaussian noise reduction, and wavelet-based denoising using wdenoise2() from the Wavelet Toolbox. Finally, image visualization and comparative analysis are performed using imshow() for display and psnr()/ssim() functions for quantitative quality assessment. The implementation includes parameter tuning for optimal noise suppression while preserving image details.