2019/12/10 by Ali Bereyhi, Bereyhi, Ali, Ralf R. Müller +1
Engineering · #Electrical and Bioimpedance Tomography #FOS: Computer and information sciences #Information Theory (cs.IT) #Photoacoustic and Ultrasonic Imaging #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1912.04572
openalex publication_date 2019/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In oversampled adaptive sensing (OAS), noisy measurements are collected in\nmultiple subframes. The sensing basis in each subframe is adapted according to\nsome posterior information exploited from previous measurements. The framework\nis shown to significantly outperform the classic non-adaptive compressive\nsensing approach.\n This paper extends the notion of OAS to signals with structured sparsity. We\ndevelop a low-complexity OAS algorithm based on structured orthogonal sensing.\nOur investigations depict that the proposed algorithm outperforms the\nconventional non-adaptive compressive sensing framework with group LASSO\nrecovery via a rather small number of subframes.\n