Image Deblurring Using Four Core Deconvolution Functions

This example showcases four different deconvolution functions for addressing image deblurring challenges. These MATLAB functions - deconvwnr, deconvreg, deconvlucy, and deconvblind - each utilize unique deconvolution algorithms and are designed for specific application scenarios with varying restoration capabilities for blurred images.

The deconvwnr function implements Wiener filter deconvolution, primarily targeting additive Gaussian noise removal through frequency-domain optimization. The deconvreg function employs constrained least squares regularization, making it suitable for images with stationary noise patterns. The deconvlucy function utilizes the Richardson-Lucy algorithm, which excels at handling motion blur through iterative maximum likelihood estimation. Meanwhile, deconvblind performs blind deconvolution using iterative techniques to estimate both the original image and unknown point spread function (PSF) simultaneously. By leveraging these specialized functions with their distinct algorithmic implementations, various types of blurred images can be processed more effectively to achieve superior restoration results.