Sparse image measurement using deep compressed sensing to accelerate image acquisition in 3D XRM

Abstract

This paper proposes the Sparse Matrix Deep Compressed Sensing (SM-DCS) that leverages on compressive sensing and deep learning techniques for 3D X-ray Microscopy (XRM) based applications. It enables up to 85% reduction in the number of pixels to be measured while maintaining reasonable accurate image quality. Unlike other direct compressed sensing approaches, SM-DCS can be applied using existing measurement equipment. SM-DCS works by measuring a subset of the image pixels followed by performing compressed sensing recovery process to recover each image slice. Experimental results demonstrate that SM-DCS produces reconstruction images that are comparable to direct compressed sensing measurement approach on various performance metrics, but without the need to change the existing equipment.