SSM-Net: Enhancing Compressed Sensing Image Reconstruction with Mamba Architecture and Fast Iterative Shrinking Threshold Algorithm Optimization
Compressed sensing (CS) is a powerful technique that can reduce data size while maintaining high reconstruction quality, which makes it particularly valuable in high-dimensional image applications. However, many existing methods have difficulty balancing reconstruction accuracy, computational effici...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/4/1026 |
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| Summary: | Compressed sensing (CS) is a powerful technique that can reduce data size while maintaining high reconstruction quality, which makes it particularly valuable in high-dimensional image applications. However, many existing methods have difficulty balancing reconstruction accuracy, computational efficiency, and fast convergence. To address these challenges, this paper proposes SSM-Net, a novel framework that combines the state-space modeling (SSM) of the Mamba architecture with the fast iterative shrinking threshold algorithm (FISTA). The Mamba-based SSM module can effectively capture local and global dependencies with linear computational complexity and significantly reduces the computation time compared to Transformer-based methods. In addition, the momentum update inspired by FISTA improves the convergence speed during deep iterative reconstruction. SSM-Net features a lightweight sampling module for efficient data compression, an initial reconstruction module for fast approximation, and a deep reconstruction module for iterative refinement. Extensive experiments on various benchmark datasets show that SSM-Net achieves state-of-the-art reconstruction performance while reducing both training and inference reconstruction time, making SSM-Net a scalable and practical solution for real-time applications of compressed sensing. |
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| ISSN: | 1424-8220 |