Constrained Least Squares Filtering for Image Restoration

This article introduces constrained least squares filtering, a powerful technique for image restoration that reconstructs corrupted images to recover their original clarity and detail. The algorithm works by formulating the restoration problem as an optimization task that minimizes noise while preserving image features through regularization constraints. To further enhance restoration quality, we implement the edgetaper function which preprocesses image boundaries using point spread function (PSF) information to reduce boundary artifacts. This combination effectively addresses common issues like noise amplification and ringing phenomena that typically occur during deconvolution processes. Through MATLAB implementation, we demonstrate how to apply these techniques using built-in functions like deconvreg for constrained deconvolution and edgetaper for boundary handling. These computational approaches enable more effective image data processing, providing researchers with more accurate and reliable data foundations for future studies.