MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network
<|reference_start|>MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network: Conventional compressed sensing (CS) algorithms typically apply a uniform sampling rate to different image blocks. A more strategic approach could be to allocate the number of measurements adaptively, based on each image block's complexity. In this paper, we propose a Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network (MB-RACS) framework, which aims to adaptively determine the sampling rate for each image block in accordance with traditional measurement bounds theory. Moreover, since in real-world scenarios statistical information about the original image cannot be directly obtained, we suggest a multi-stage rate-adaptive sampling strategy. This strategy sequentially adjusts the sampling ratio allocation based on the information gathered from previous samplings. We formulate the multi-stage rate-adaptive sampling as a convex optimization problem and address it using a combination of Newton's method and binary search techniques. Additionally, we enhance our decoding process by incorporating skip connections between successive iterations to facilitate a richer transmission of feature information across iterations. Our experiments demonstrate that the proposed MB-RACS method surpasses current leading methods, with experimental evidence also underscoring the effectiveness of each module within our proposed framework.<|reference_end|>
@article{huang2024mb-racs:,
    title={MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing
  Network},
    author={Yujun Huang, Bin Chen, Naiqi Li, Baoyi An, Shu-Tao Xia, and Yaowei
  Wang},
    journal={arXiv preprint arXiv:2402.16855},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16855},
    primaryClass={cs.CV}
}