The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing
Abstract
Compressive sensing (CS) is an effective technique for reconstructing image from a small amount of sampled data. It has been widely applied in medical imaging, remote sensing, image compression, etc. In this paper, we propose two versions of a novel deep learning architecture, dubbed as ADMM-CSNet, by combining the traditional model-based CS method and data-driven deep learning method for image reconstruction from sparsely sampled measurements. We first consider a generalized CS model for image reconstruction with undetermined regularizations in undetermined transform domains, and then two efficient solvers using Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing the model are proposed. We further unroll and generalize the ADMM algorithm to be two deep architectures, in which all parameters of the CS model and the ADMM algorithm are discriminatively learned by end-to-end training. For both applications of fast CS complex-valued MR imaging and CS imaging of real-valued natural images, the proposed ADMM-CSNet achieved favorable reconstruction accuracy in fast computational speed compared with the traditional and the other deep learning methods.
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Cites
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Cited by
LRR-Net: An Interpretable Deep Unfolding Network for Hyperspectral Anomaly Detection
Li, C., Zhang, B., Hong, D., Yao, J., & Chanussot, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3279834 · Cited by 270
Algorithm-Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks
Xu, W., An, J., Li, H., Gan, L., & Yuen, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288072 · Cited by 49
Self-citation
Metadata
- Authors
- Yang, Y., Sun, J., Li, H., & Xu, Z.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2020-03-01
- Volume
- 42
- Issue
- 3
- Pages
- 521-538
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2902719825
- Cited by
- 908
- References
- 62
- FWCI
- 36.84
- Type
- journal article
- Language
- en
- Open access
- closed
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