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.

本文提出了一种基于测量上界理论的率自适应图像压缩感知网络 (MB-RACS) 框架，其旨在根据传统测量上界理论自适应确定每个图像块的采样率。此外，我们还提出了一种多阶段率自适应采样策略，通过前几次采样获得的信息顺序调整采样率分配，并使用牛顿法和二分搜索技术解决该问题。与现有方法相比，实验证明了所提出的 MB-RACS 方法的优越性，并且实验证据还强调了我们提出框架中每个模块的有效性。