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GSISTA-Net: generalized structure ISTA networks for image compressed sensing based on optimized unrolling algorithm

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Abstract

Image compressed sensing technology, particularly algorithm unrolling networks, has garnered significant attention in the field of compressed sensing due to their interpretability and high performance. However, similar to traditional compressed sensing methods, algorithm unrolling networks update and transmit pixel-level image data through specific instances of algorithms, often failing to fully exploit the rich information encoded in image features. This limitation results in information loss and incomplete feature fusion. In this paper, we introduce a novel approach: the Generalized Structure Iterative Shrinkage Threshold Algorithm (GSISTA), and present an algorithm extension network built upon GSISTA, referred to as GSISTA-Net. GSISTA-Net facilitates the efficient transfer of image feature information during the deep reconstruction stage through a jump connection structure. Additionally, it incorporates a dual-scale denoising module within the deep reconstruction stage to enhance denoising effectiveness. Our experimental results demonstrate that the proposed method surpasses the performance of five prominent state-of-the-art algorithms, specifically ReconNet, CSNet, ISTA-Net\(^+\), AMPNet, and OPINE-Net\(^+\), when evaluated in terms of both Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).

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Acknowledgements

The research work of this paper were supported by the National Natural Science Foundation of China (No. 62177022, 61901165, 61501199), Natural Science Foundation of Hubei Province (No. 2022CFA007), Collaborative Innovation Center for Informatization and Balanced Development of K-12 Education by MOE and Hubei Province (No. xtzd2021-005), and Wuhan Knowledge Innovation Project (No.2022020801010258).

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Authors and Affiliations

  1. Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Nanli Road, Wuhan, 430068, China

    Chunyan Zeng, Yan Yu, Shiyan **a, Hao Cui & **angkui Wan

  2. Department of Digital Media Technology, Central China Normal University, Luoyu Road, Wuhan, 430079, China

    Zhifeng Wang

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  1. Chunyan Zeng
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  2. Yan Yu
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  3. Zhifeng Wang
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  4. Shiyan **a
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  5. Hao Cui
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  6. **angkui Wan
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Correspondence to Zhifeng Wang.

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Zeng, C., Yu, Y., Wang, Z. et al. GSISTA-Net: generalized structure ISTA networks for image compressed sensing based on optimized unrolling algorithm. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18724-9

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