image deconvolution under poisson noise using sparse representations and proximal thresholding iteration (Starck Inc)
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Starck Inc
image deconvolution under poisson noise using sparse representations and proximal thresholding iteration
Image Deconvolution Under Poisson Noise Using Sparse Representations And Proximal Thresholding Iteration, supplied by Starck Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/image+deconvolution+under+poisson+noise+using+sparse+representations+and+proximal+thresholding+iteration/image+deconvolution+under+poisson+noise+using+sparse+representations+and+proximal+thresholding+iteration/10__1049_slash_iet___cta__2015__1000-213-18-6
Average 90 stars, based on 1 article reviews
Image Deconvolution Under Poisson Noise Using Sparse Representations And Proximal Thresholding Iteration, supplied by Starck Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/image+deconvolution+under+poisson+noise+using+sparse+representations+and+proximal+thresholding+iteration/image+deconvolution+under+poisson+noise+using+sparse+representations+and+proximal+thresholding+iteration/10__1049_slash_iet___cta__2015__1000-213-18-6
Average 90 stars, based on 1 article reviews
image deconvolution under poisson noise using sparse representations and proximal thresholding iteration - by Bioz Stars,
2026-09
90/100 stars
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Related Articles
other:Article Title: Mean‐square filtering for polynomial discrete‐time systems with Poisson noises Article Snippet: The discrete–time state estimation problem for a class of stochastic nonlinear polynomial systems confused with Poisson noises over linear observations is presented in this paper.. The filtering problem is solved computing the time–update and measurement–update equations for the state estimate and error covariance matrix.. A finite number of filtering equations can be obtained by expressing the conditional expectations of polynomial terms as functions of the estimate and error covariance. |