Patterns (Jul 2021)
Super-compression of large electron microscopy time series by deep compressive sensing learning
Abstract
Summary: 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 encoding power, memory, and transmission bandwidth can be saved, allowing it to be deployed to existing detectors. We anticipate the proposed technique will have far-reaching applications in edge computing for EM and other imaging techniques. The bigger picture: The rapid development of electron microscopy (EM) opens a new door to exploring physical sciences; however, it raises grand challenges and urgent needs for big data processing. Therefore, it is crucial to compress the EM data. But existing compression methods developed for natural images do not perform well in EM images. In this paper, by combining deep learning and temporal compressive sensing, we propose a novel compression strategy specifically for EM data processing. Owing to the improved compression efficiency and built-in denoising capability of our framework over JPEG compression, compressed videos with compression ratio of 30 can be reconstructed with high fidelity. Therefore, considerable (encoding) power, in situ memory, and transmission bandwidth are expected to be saved. In the future, we will strive to increase the compression ratio without reducing the reconstruction quality. And we believe our proposed EM compression method has a wide application for the EM community.
