Multi-Scale Deep Compressive Imaging
- Authors
- Canh, T.N.[Canh, T.N.]; Jeon, B.[Jeon, B.]
- Issue Date
- 2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- compressive sensing; convolution neural network; Deep learning; image decomposition; multi-scale
- Citation
- IEEE Transactions on Computational Imaging, v.7, pp.86 - 97
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Computational Imaging
- Volume
- 7
- Start Page
- 86
- End Page
- 97
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/25244
- DOI
- 10.1109/TCI.2020.3034433
- ISSN
- 2573-0436
- Abstract
- 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. © 2015 IEEE.
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