MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network
This work addresses image compression efficiency for applications like medical imaging or video streaming, but it is incremental as it builds on existing measurement bounds theory with a novel multi-stage strategy.
The paper tackled the problem of inefficient uniform sampling in compressed sensing by proposing a rate-adaptive network (MB-RACS) that allocates measurements based on image block complexity, achieving superior performance over leading methods.
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.