Explore MATLAB source code curated for "去模糊" with clean implementations, documentation, and examples.
MATLAB implementation of iterative blind deconvolution algorithm for image deblurring in digital image processing, featuring complete source code for restoring blurred images to sharp, recognizable quality
This is a sparse representation-based image reconstruction algorithm designed for image deblurring and super-resolution applications, featuring practical implementation with code examples demonstrating dictionary learning and sparse coding techniques.
A comprehensive software package for Total Variation-based image reconstruction, featuring implementations for denoising, inpainting, and deblurring operations. The package employs first-order optimization algorithms and incorporates regularization techniques to preserve edges while removing noise and artifacts. Reference: J. Dahl, P. C. Hansen, S. H. Jensen, and T. L. Jensen, "Algorithms and Software for Total Variation Image Reconstruction via First-Order Methods," Numerical Algorithms, vol. 53, 2010, pp. 67-92.
The application of the Split Bregman method in image processing demonstrates superior performance compared to traditional deblurring techniques, with enhanced edge preservation and noise reduction capabilities.
Implementing Focus-Deblur Techniques with Algorithmic Approaches and Code Implementation Details