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Information Bottleneck Measurement for Compressed Sensing Image Reconstruction

Authors
Lee, BokyeungKo, KyungdeukHong, JonghwanKu, BonhwaKo, Hanseok
Issue Date
2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Sensors; Generators; Decoding; Training; Image reconstruction; Image coding; Loss measurement; Information bottleneck; image compressed sens- ing; deep learning
Citation
IEEE SIGNAL PROCESSING LETTERS, v.29, pp.1943 - 1947
Indexed
SCIE
SCOPUS
Journal Title
IEEE SIGNAL PROCESSING LETTERS
Volume
29
Start Page
1943
End Page
1947
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/145575
DOI
10.1109/LSP.2022.3205275
ISSN
1070-9908
Abstract
Image Compressed Sensing (CS) has achieved a lot of performance improvement thanks to advances in deep networks. The CS method is generally composed of a sensing and a decoder. The sensing and decoder networks have a significant impact on the reconstruction performance, and it is obvious that both two networks must be in harmony. However, previous studies have focused on designing the loss function considering only the decoder network. In this paper, we propose a novel training process that can learn sensing and decoder networks simultaneously using Information Bottleneck (IB) theory. By maximizing importance through proposed importance generator, the sensing network is trained to compress important information for image reconstruction of the decoder network. The representative experimental results demonstrate that the proposed method is applied in recently proposed CS algorithms and increases the reconstruction performance with large margin in all CS ratios.
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