Example of Image Restoration Using Blind Deconvolution

This example presents a demonstration of image restoration using blind deconvolution technique, featuring Chinese annotations for better understanding. The implementation shows how blind deconvolution can recover blurred images and restore them to their original clarity. Blind deconvolution is an advanced image processing technique that estimates both the original sharp image and the blur kernel simultaneously from a degraded input image. In typical implementations, algorithms like the Maximum A Posteriori (MAP) estimation or iterative methods such as Lucy-Richardson deconvolution with blind kernel estimation are employed. The process generally involves: 1. Initial blur kernel estimation using edge detection or statistical methods 2. Alternating optimization between image restoration and kernel refinement 3. Regularization techniques to prevent noise amplification Key MATLAB functions that may be used include deconvblind for blind deconvolution operations, where parameters like initial point spread function (PSF) estimates and iteration numbers can be optimized. The algorithm typically handles various blur types including motion blur, Gaussian blur, and defocus blur. This example helps users better understand the application and effectiveness of blind deconvocation technology in practical image processing scenarios, ultimately improving analysis accuracy and processing outcomes. Through proper parameter tuning and algorithm selection, significant improvements in image quality can be achieved.