Reconstruction-free Cascaded Adaptive Compressive Sensing

Scene-aware Adaptive Compressive Sensing (ACS) has constituted a persistent pursuit, holding substantial promise for the enhancement of Compressive Sensing (CS) performance. Cascaded ACS furnishes a proficient multi-stage framework for adaptively allocating the CS sampling based on previous CS measurements. However, reconstruction is commonly required for analyzing and steering the successive CS sampling, which bottlenecks the ACS speed and impedes the practical application in time-sensitive scenarios. Addressing this challenge, we propose a reconstruction-free cascaded ACS method, which requires NO reconstruction during the adaptive sampling process. A lightweight Score Network (ScoreNet) is proposed to directly determine the ACS allocation with previous CS measurements and a differentiable adaptive sampling module is proposed for end-to-end training. For image reconstruction, we propose a Multi-Grid Spatial-Attention Network (MGSANet) that could facilitate efficient multi-stage training and inferencing. By introducing the reconstruction-fidelity supervision outside the loop of the multi-stage sampling process, ACS can be efficiently optimized and achieve high imaging fidelity. The effectiveness of the proposed method is demonstrated with extensive quantitative and qualitative experiments, compared with the state-of-the-art CS algorithms.