AdaCS: Adaptive Compressive Sensing with Restricted Isometry Property-Based Error-clamping
IEEE Transactions on Pattern Analysis and Machine Intelligence(2024)
Abstract
Scene-dependent adaptive compressive sensing (CS) has been a long pursuing goal that has huge potential to significantly improve the performance of CS. However, with no access to the ground truth, how to design the scene-dependent adaptive strategy is still an open problem. In this paper, a restricted isometry property (RIP) condition-based error-clamping is proposed, which could directly predict the reconstruction error, i.e., the difference between the current-stage reconstructed image and the ground truth image, and adaptively allocate more samples to regions with larger reconstruction error at the next sampling stage. Furthermore, we propose a CS reconstruction network composed of Progressively inverse transform and Alternating Bi-directional Multi-grid Network, named PiABM-Net, that could efficiently utilize the multi-scale information for reconstructing the target image. The effectiveness of the proposed adaptive and cascaded CS method is demonstrated with extensive quantitative and qualitative experiments, compared with the state-of-the-art CS algorithms.
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Key words
Image reconstruction,Imaging,Measurement uncertainty,Magnetic resonance imaging,Adaptive systems,Loss measurement,Laplace equations,Adaptive compressive sensing,multi-scale information,restricted isometry property
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