MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network

• URL: http://arxiv.org/abs/2402.16855v1
• Date: Fri, 19 Jan 2024 04:40:20 GMT
• Title: MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network
• Authors: Yujun Huang, Bin Chen, Naiqi Li, Baoyi An, Shu-Tao Xia, Yaowei Wang,
• Abstract summary: We propose a Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network (MB-RACS) framework. Our experiments demonstrate that the proposed MB-RACS method surpasses current leading methods.
• Score: 65.1004435124796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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