Low-dose imaging denoising with one pair of noisy images

Author:

Yang Dongyu1,Lv Wenjin,Zhang Junhao,Chen Hao,Sun Xinkai2,Lv Shenzhen3,Dai Xinzhe2,Luo Ruichun2,Zhou Wu2,Qiu Jisi1,Shi Yishi1ORCID

Affiliation:

1. Chinese Academy of Sciences

2. University of Chinese Academy of Sciences

3. Nanyang Technological University

Abstract

Low-dose imaging techniques have many important applications in diverse fields, from biological engineering to materials science. Samples can be protected from phototoxicity or radiation-induced damage using low-dose illumination. However, imaging under a low-dose condition is dominated by Poisson noise and additive Gaussian noise, which seriously affects the imaging quality, such as signal-to-noise ratio, contrast, and resolution. In this work, we demonstrate a low-dose imaging denoising method that incorporates the noise statistical model into a deep neural network. One pair of noisy images is used instead of clear target labels and the parameters of the network are optimized by the noise statistical model. The proposed method is evaluated using simulation data of the optical microscope, and scanning transmission electron microscope under different low-dose illumination conditions. In order to capture two noisy measurements of the same information in a dynamic process, we built an optical microscope that is capable of capturing a pair of images with independent and identically distributed noises in one shot. A biological dynamic process under low-dose condition imaging is performed and reconstructed with the proposed method. We experimentally demonstrate that the proposed method is effective on an optical microscope, fluorescence microscope, and scanning transmission electron microscope, and show that the reconstructed images are improved in terms of signal-to-noise ratio and spatial resolution. We believe that the proposed method could be applied to a wide range of low-dose imaging systems from biological to material science.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Fusion Foundation of Research and Education of CAS

University of Chinese Academy of Sciences

Fundamental Research Funds for the Central Universities

Funded Project of Hebei Province Innovation Capability Improvement Plan, China

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics

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