Super-compression of large electron microscopy time series by deep compressive sensing learning.

ABSTRACT: The development of ultrafast detectors for electron microscopy (EM) opens a new door to exploring dynamics of nanomaterials; however, it raises grand challenges for big data processing and storage. Here, we combine deep learning and temporal compressive sensing (TCS) to propose a novel EM big data compression strategy. Specifically, TCS is employed to compress sequential EM images into a single compressed measurement; an end-to-end deep learning network is leveraged to reconstruct the original images. Owing to the significantly improved compression efficiency and built-in denoising capability of the deep learning framework over conventional JPEG compression, compressed videos with a compression ratio of up to 30 can be reconstructed with high fidelity. Using this approach, considerable enc

SUBMITTER: Zheng S 

PROVIDER: S-EPMC8276025 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature