MATLAB Implementation of Digital Image Restoration and Deconvolution

The MATLAB-implemented digital image restoration and deconvolution programs serve as highly valuable tools for processing and enhancing digital images. These programs enable quality restoration of degraded images through deconvolution operations that effectively reverse blurring effects caused by various factors. The implementation includes comprehensive examples demonstrating several widely-used deconvolution methodologies, such as Wiener filtering for noise-robust restoration, the Richardson-Lucy algorithm for iterative maximum likelihood estimation, and constrained least squares methods for regularization-based solutions. Each method is accompanied by MATLAB code showcasing practical implementation aspects, including point spread function (PSF) modeling, noise parameter configuration, and convergence criteria settings. These programs significantly contribute to both academic research and engineering applications by providing hands-on experience with image restoration techniques, thereby enhancing understanding and proficiency in digital image processing and deconvolution methodologies. The code structure emphasizes modular design, allowing users to easily modify parameters and experiment with different degradation models while maintaining computational efficiency through optimized matrix operations and built-in MATLAB image processing functions.