Algorithm Explanation and Code Examples for Blind Deconvolution

This article provides comprehensive explanations and practical code examples for implementing blind deconvolution algorithms. These resources are particularly valuable for readers seeking to understand and apply this advanced signal processing technique. Since blind deconvolution represents a relatively novel concept for many practitioners, the article offers in-depth guidance and explanations covering both theoretical foundations and practical implementation. The content includes detailed algorithm descriptions, such as iterative optimization approaches and maximum likelihood estimation methods, along with code examples demonstrating key functions like point spread function estimation and image restoration techniques. Through these resources, readers can learn how to apply blind deconvolution to solve real-world problems, such as image deblurring and system identification, while gaining practical experience in algorithm implementation. The examples showcase MATLAB or Python code structures for implementing critical components including regularization techniques, convolution matrix operations, and convergence criteria. Overall, this article serves as an excellent technical resource worth careful study and exploration for anyone working in digital signal processing or computational photography.