Image Restoration, Degradation Models, Continuous Degradation Models, Discrete Degradation Models, Algebraic Restoration Methods, Algebraic Restoration Principles, Inverse Filtering Restoration, Least Squares Filtering
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Resource Overview
Image Restoration, Degradation Models, Continuous Degradation Models, Discrete Degradation Models, Algebraic Restoration Methods, Algebraic Restoration Principles, Inverse Filtering Restoration, Least Squares Filtering, MATLAB Implementation of Image Restoration, Wiener Filter Restoration, Regularized Filter Restoration, Lucy-Richardson Restoration, Blind Deconvolution Restoration, Other MATLAB Functions for Image Restoration
Detailed Documentation
Image restoration is a process of recovering image quality, involving various types of degradation models and restoration methods. Degradation models can be categorized into continuous degradation models and discrete degradation models. Restoration methods employ algebraic approaches, such as inverse filtering restoration and least squares filtering. In MATLAB implementation, different functions are available for image restoration, including Wiener filter restoration (using the wiener2 function for noise reduction), regularized filter restoration (applying constraints to stabilize solutions), and Lucy-Richardson restoration (utilizing the deconvlucy function for iterative deconvolution). Additionally, other MATLAB functions related to image restoration, such as blind deconvolution (deconvblind for estimating point spread functions), can be applied to enhance image quality through algorithmic processing.
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