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Paper Abstract and Keywords
Presentation 2019-12-05 11:40
[Special Talk] Representation of moving-image's sparsity and its applications to adaptive moving-image restoration
Takahiro Saito (Kanagawa Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) This talk states that statistical sparsity of a moving-image sequence can be properly represented in the domain of the 3-D mean-separation-type short-time DFT (3-D MS2T-DFT). Moreover, taking account of the probability distribution of additive noise, this talk addresses the problem how to construct shrinkage functions, which are used to remove effects of the noise in the 3-D MS2T-DFT domain selectively, as the Bayes LS estimators, and demonstrates that the adaptive application of the shrinkage functions leads to the accomplishment of the state-of-the-art moving-image restoration in the restoration tasks of color moving-image denoising, Poisson-Gaussian color moving-image restoration, and single-plate color-mosaicked moving-image restoration.
Keyword (in Japanese) (See Japanese page) 
(in English) Brain Neuroscience / Sparse Coding / Statistical Modeling / Signal Restoration / Moving-Image Processing / / /  
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Conference Information
Conference Date 2019-12-05 - 2019-12-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Aiina Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image coding, Communications and streaming technologies, etc. 
Paper Information
Registration To IEICE-IE 
Conference Code 2019-12-IE-CS-AVM-BCT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Representation of moving-image's sparsity and its applications to adaptive moving-image restoration 
Sub Title (in English)  
Keyword(1) Brain Neuroscience  
Keyword(2) Sparse Coding  
Keyword(3) Statistical Modeling  
Keyword(4) Signal Restoration  
Keyword(5) Moving-Image Processing  
1st Author's Name Takahiro Saito  
1st Author's Affiliation Kanagawa University (Kanagawa Univ.)
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Speaker Author-1 
Date Time 2019-12-05 11:40:00 
Presentation Time 60 minutes 
Registration for IEICE-IE 
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Volume (vol) vol.43 
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