SAU-Net: Saliency-Based Adaptive Unfolding Network for Interpretable High-Quality Image Compressed Sensing in Internet of Things

Deep unfolding networks have emerged as a prominent approach for IoT image Compressed Sensing (CS) due to their interpretability and exceptional performance, leveraging iterative optimization algorithms as design guides. However, the uniform sampling strategy fails to adapt to the diverse information density distributions of image blocks, resulting in inadequate information extraction. Additionally, the reconstruction network neglects to integrate the characteristics of the sampling network, hindering the establishment of an adaptive CS framework based on end-to-end deep neural networks. In light of these limitations, this paper presents a novel image CS network called the Saliency-based Adaptive Unfolding Network (SAU-Net), which addresses non-uniform sampling and adaptive image reconstruction. During the sampling phase, the saliency-based non-uniform sampling module is devised to extract input image information comprehensively by dynamically assigning sampling rates based on block saliency. Subsequently, the reconstruction phase incorporates a saliency enhancement block that augments feature representation and image reconstruction capabilities by amplifying the saliency-based deep reconstruction features. To facilitate efficient end-to-end sampling and reconstruction, we introduce an adaptive multi-channel soft threshold block that tailors the soft thresholding function to multi-channel features, thereby enhancing the function’s generalization capability. Extensive experiments on three benchmark datasets, Set11, CBSD68 and Urban100, validate the superior performance of the proposed SAU-Net compared to state-of-the-art CS methods. Our code is publicly available at https://github.com/CCNUZFW/SAU-Net.