Journal
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
Volume 7, Issue -, Pages 86-97Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCI.2020.3034433
Keywords
Deep learning; compressive sensing; multi-scale; image decomposition; convolution neural network
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Science and ICT [NRF2020R1A2C2007673, NRF-2017R1A2B2006518]
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Recent research has shown that deep learning-based multi-scale compressive imaging outperforms conventional methods in terms of reconstruction quality and speed, and the efficiency and performance of multi-scale sampling can be further improved by jointly learning to decompose, sample, and reconstruct images.
Recently, deep learning-based compressive imaging (DCI) has surpassed conventional compressive imaging in reconstruction quality and running speed. While multi-scale sampling has shown superior performance over single-scale, research in DCI has been limited to single-scale sampling. Despite training with single-scale images, DCI tends to favor low-frequency components similar to conventional multi-scale sampling, especially at low subrates. From this perspective, it would be easier for the network to learn multi-scale features with a multi-scale sampling architecture. In this work, we propose a multi-scale deep compressive imaging (MS-DCI) framework which jointly learns to decompose, sample, and reconstruct images at multi-scale. A three-phase end-to-end training scheme is introduced with an initial and two enhanced reconstruction phases to demonstrate the efficiency of multi-scale sampling and further improve the reconstruction performance. We analyze the decomposition methods (including pyramid, wavelet, and scale-space), sampling matrices, and measurements and show the empirical benefit of MS-DCI, which consistently outperforms both conventional and deep learning-based approaches.
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