Solar image reconstruction by phase diversity with low rank prior

Wang Shuai; Bao Hua; He Chunyuan; Rong Huiqin; Li Shuqi; Hou Jialin; Rao Changhui

doi:10.12086/oee.2023.220208

Article navigation > Opto-Electronic Engineering > 2023 Vol. 50 > No. 6 > 220208

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Wang S, Bao H, He C Y, et al. Solar image reconstruction by phase diversity with low rank prior[J]. Opto-Electron Eng, 2023, 50(6): 220208. doi: 10.12086/oee.2023.220208

Citation:

Wang S, Bao H, He C Y, et al. Solar image reconstruction by phase diversity with low rank prior[J]. Opto-Electron Eng, 2023, 50(6): 220208. doi: 10.12086/oee.2023.220208

Solar image reconstruction by phase diversity with low rank prior

1.
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
2.
Yangtze Delta Region Institute of University of Electronic Science and Technology of China, Quzhou, Zhejiang 324003, China
3.
Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
4.
University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
5.
University of Chinese Academy of Sciences, Beijing 100049, China
6.
66389 Troops, Zhengzhou, Henan 450000, China

Fund Project: Project supported by National Natural Science Foundation of China (11727805, 11703029, 11733005)

More Information

^*Corresponding author: hbao@ioe.ac.cn

Received Date 28 August 2022

Revised Date 07 February 2023

Accepted Date 15 February 2023

Published Date 25 June 2023

Abstract

Abstract

Phase diversity is one of the commonly used image post-reconstruction methods. In order to improve the robustness of phase diversity for solar image reconstruction, this paper proposes an improved phase diversity method based on the low-rank prior, i.e., the nuclear norm regularization of the image is introduced into the phase diversity model, and the image sub-model and the phase sub-model are solved by the half-quadratic splitting method and BFGS respectively. Reconstruction experiments and analysis are carried out on the simulated degraded focused and defocused solar images. Compared with the classical phase diversity based on Tikhonov regularization, the phase diversity based on low-rank prior can improve the accuracy of wavefront phase estimation and the quality of reconstructed images in terms of subjective visual effects and objective indexes in both the noise-free and noise-included cases.
- phase diversity /
- low-rank prior /
- solar images

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References

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Overview

Overview

Atmospheric turbulence reduces the imaging resolution of ground-based optical telescopes and seriously affects astronomical observations. At present, the most effective solution to this problem is adaptive optics (AO) technology combined with image post-reconstruction. Most large-aperture solar telescopes at home and abroad are equipped with AO systems, and the collected solar (adaptive optics) images can be further reconstructed by blind deconvolution, phase diversity, speckle reconstruction, or deep learning. Among them, the phase diversity is to add one or more additional imaging channels in the imaging system and use a set of images (focused and defocused images) of different imaging channels of the same object collected at the same time to estimate the wavefront aberration, and then restore the object image based on the wavefront aberration.

Phase diversity (PD) is essentially a more constrained multi-channel blind deconvolution method with higher reliability. However, the traditional phase diversity has poor robustness and can be easily affected by image noise and initial value, which makes the optimization process fall into local minimum and even leads to a lot of ringing artifacts in the reconstructed image. For this problem, the common solution is to use multiple sets of focused and defocused images for phase diversity, i.e., the phase-diverse speckle (PDS). The complementarity of high-frequency information between multiple sets of focused and defocused images can reduce the influence of image noise and wavefront phase estimation errors in the iterative process of the algorithm. However, in some practical situations, it is inconvenient to obtain multiple sets of focused and defocused images in which the object remains stationary, so it is difficult to apply the phase-diverse speckle method.

Inspired by the low-rank prior widely used in the robust principal component analysis (RPCA), noise and ringing artifacts are interference components that destroy the low-rank property of the original image. In order to improve the robustness of phase diversity for a single set of focused and defocused solar images, this paper proposes an improved phase diversity based on low rank prior, i.e., a new phase diversity model based on the nuclear norm regularization of the image is established, and the image sub-model and the phase sub-model are solved by the half-quadratic splitting method and BFGS respectively. Finally, reconstruction experiments and analysis are carried out on the simulated focused and defocused solar images. Compared with the classical phase diversity based on Tikhonov regularization, the phase diversity based on low rank prior can improve the accuracy of wavefront phase estimation and the quality of reconstructed images in terms of subjective visual effects and objective indexes in both the noise-free and noise-included cases.