Blind Deconvolution for Image Restoration

Research on blind deconvolution for image restoration is critically important in the field of computational photography. In this study, we focus on developing novel algorithms and techniques to enhance image restoration quality through iterative optimization methods. We explore various blind deconvolution approaches including maximum a posteriori (MAP) estimation and variational Bayesian methods, implementing them using Python/Matlab with key functions like Richardson-Lucy deconvolution and Weiner filtering. Our implementation typically involves optimizing point spread function (PSF) estimation through gradient descent algorithms while applying regularization techniques to handle noise and artifacts. Through practical applications on real-world images, we aim to provide robust image restoration solutions adaptable to diverse scenarios such as medical imaging and astronomical photography. Furthermore, we investigate potential applications including image enhancement and corrupted data recovery, where our code architecture incorporates multi-scale processing and automatic parameter tuning. We believe this research will drive breakthroughs in computational imaging through improved blind deconvolution techniques that efficiently handle motion blur and out-of-focus artifacts.