Explore MATLAB source code curated for "图像恢复" with clean implementations, documentation, and examples.
This MATLAB source code implements a blind deconvolution iterative algorithm for image restoration, which provides significant benefits for digital image processing education and practical implementation with built-in algorithmic demonstrations.
This implementation demonstrates both least squares filtering and Wiener filtering for image restoration, displaying comparative result plots for analytical evaluation
Implementation of Markov Random Fields for Image Restoration, including MRF generation algorithms and applications in image denoising with code-level implementation details
Image restoration based on Markov Random Fields, including program implementation for generating MRF models and application examples for image denoising
Blind Iterative Deconvolution Algorithm enables image restoration without requiring prior Point Spread Function (PSF) knowledge
Implementation of Orthogonal Matching Pursuit (OMP) algorithm for compressed sensing in image compression and reconstruction with code-level insights.
Utilizing block sparsity approaches to solve sensing matrices with applications in compressed sensing and image restoration
Restoration of more realistic motion-blurred images, specifically limited to horizontally or vertically blurred images. Key reference: "Removing Camera Shake from a Single Image" from SIGGRAPH 2006. The implementation typically involves using deconvolution algorithms with point spread function (PSF) estimation to reverse motion blur effects.
This file provides source code for image restoration and recovery, specifically designed to handle issues like motion blur and noise interference. The implementation includes algorithms for enhancing image quality and reconstructing lost details.
A blind deconvolution-based phase difference algorithm for image restoration, including simulation implementations and L-BFGS optimization algorithm with code demonstrations