Algorithm Based on Bayesian Least Squares - Gaussian Scale Mixture Model
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Resource Overview
BLS-GSM stands for "Bayesian Least Squares - Gaussian Scale Mixture", representing an image denoising algorithm utilizing Bayesian least squares estimation with Gaussian scale mixture priors. This implementation employs multi-scale wavelet decomposition and Bayesian estimation techniques to effectively reduce image noise while preserving edge details.
Detailed Documentation
This paper introduces the BLS-GSM image denoising algorithm, where BLS-GSM denotes "Bayesian Least Squares - Gaussian Scale Mixture". The algorithm leverages Bayesian least squares estimation combined with Gaussian scale mixture models to effectively reduce noise in digital images. The implementation typically involves wavelet coefficient modeling where noise components are separated from image signals through probabilistic mixture distributions. For detailed technical specifications and referenced literature, please consult the documentation in the readme file. Although this algorithm has been extensively researched and applied in image processing, significant opportunities for improvement and development remain, and we anticipate future research will explore more advanced implementations incorporating adaptive thresholding and machine learning enhancements.
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