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

Ying Hao Tan; Nicholas Vun; Bu-Sung Lee

doi:10.1117/12.2691418

28 July 2023 Sparse image measurement using deep compressed sensing to accelerate image acquisition in 3D XRM

Ying Hao Tan, Nicholas Vun, Bu-Sung Lee

Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 127490M (2023) https://doi.org/10.1117/12.2691418
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France

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.

Citation Download Citation

Ying Hao Tan, Nicholas Vun, and Bu-Sung Lee "Sparse image measurement using deep compressed sensing to accelerate image acquisition in 3D XRM", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 127490M (28 July 2023); https://doi.org/10.1117/12.2691418

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