Progressive compressive sensing of large images with multiscale deep learning reconstruction.

ABSTRACT: Compressive sensing (CS) is a sub-Nyquist sampling framework that has been employed to improve the performance of numerous imaging applications during the last 15 years. Yet, its application for large and high-resolution imaging remains challenging in terms of the computation and acquisition effort involved. Often, low-resolution imaging is sufficient for most of the considered tasks and only a fraction of cases demand high resolution, but the problem is that the user does not know in advance when high-resolution acquisition is required. To address this, we propose a multiscale progressive CS method for the high-resolution imaging. The progressive sampling refines the resolution of the image, while incorporating the already sampled low-resolution information, making the process highly effi

SUBMITTER: Kravets V 

PROVIDER: S-EPMC9068919 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature