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High-dimensional embedding denoising autoencoding prior for color image restoration

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Citation Author(s):
Yuan Yuan, Jinjie Zhou, Zhuonan He, Shanshan Wang, Biao Xiong,Qiegen Liu
Submitted by:
Qiegen Liu
Last updated:
19 September 2019 - 9:32pm
Document Type:
Presentation Slides
Document Year:
2019
Event:
Paper Code:
MP.L7.2

Abstract 

Abstract: 

This work exploits the basic denoising autoencoding (DAE) as enhanced priori for color image restoration (IR). The proposed method consists of two steps: enhanced DAE network learning and iterative restoration. To be special, at the training phase, a denoising network taking 6-dimensional variable as input is trained. Then, the network-driven high-dimensional prior information embedded DAE priori is utilized in the iterative restoration procedure. We first map the intermediate color image to be 6 dimensional and employ the higher-dimensional network to handle its corrupted version. The average operator is used to turn it back to the 3-channel image. The higher-dimensional prior alleviates the issue of the basic DAE that getting trapped in local optimal solution and effectively overcomes the instability. Experimental results on single image super-restoration (SISR) and deblurring demonstrate that the proposed algorithm can achieve good performance and prime visual inspection.

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High-dimensional embedding denoising autoencoding prior for color image restoration

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