Exploiting prior knowledge in compressed sensing to design robust systems for endoscopy image recovery

In this work, we investigate compressed sensing (CS) techniques based on the exploitation of prior knowledge to support telemedicine. In particular, prior knowledge is obtained by computing the probability of appearance of non-zero elements in each row of a sparse matrix, which is then employed in sensing matrix design and recovery algorithms for CS systems. A robust sensing matrix is designed by jointly reducing the average mutual coherence and the projection of the sparse representation error. A Probability-Driven Normalized Iterative Hard Thresholding (PD-NIHT) algorithm is developed as the recovery method, which also exploits the prior knowledge of the probability of appearance of non-zero elements and can bring performance benefits. Simulations for synthetic data and different organs of endoscopy image are carried out, where the proposed sensing matrix and PD-NIHT algorithm achieve a better performance than previously reported algorithms. (C) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

In this work, compressed sensing techniques based on prior knowledge are investigated for supporting telemedicine. The prior knowledge obtained by computing the probability of appearance of non-zero elements in each row of a sparse matrix is utilized in sensing matrix design and recovery algorithms. A robust sensing matrix is designed by jointly reducing the average mutual coherence and the projection of the sparse representation error. A Probability-Driven Normalized Iterative Hard Thresholding algorithm is developed as the recovery method, which exploits the prior knowledge and provides performance benefits. Simulations for synthetic data and endoscopy images of different organs demonstrate the superior performance of the proposed methods compared to previous algorithms.