No-Reference Quality Assessment of Extended Target Adaptive Optics Images Using Deep Neural Network

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 1; p. 1
• Main Authors: Gao, Guoqing, Li, Lingxiao, Chen, Hao, Jiang, Ning, Li, Shuqi, Bian, Qing, Bao, Hua, Rao, Changhui
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19-12-2023; MDPI
• Subjects: Algorithms; AO images; deep neural network; Image processing; image quality assessment; Imaging systems; Methods; Neural networks; Normal distribution; Optics; point spread function; Quality management; Semantics; Simulation; Simulation methods; Telescope; Telescopes; China; image quality assessment; AO images; deep neural network; point spread function
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24010001

This paper proposes a supervised deep neural network model for accomplishing highly efficient image quality assessment (IQA) for adaptive optics (AO) images. The AO imaging systems based on ground-based telescopes suffer from residual atmospheric turbulence, tracking error, and photoelectric noise, which can lead to varying degrees of image degradation, making image processing challenging. Currently, assessing the quality and selecting frames of AO images depend on either traditional IQA methods or manual evaluation by experienced researchers, neither of which is entirely reliable. The proposed network is trained by leveraging the similarity between the point spread function (PSF) of the degraded image and the Airy spot as its supervised training instead of relying on the features of the degraded image itself as a quality label. This approach is reflective of the relationship between the degradation factors of the AO imaging process and the image quality and does not require the analysis of the image's specific feature or degradation model. The simulation test data show a Spearman's rank correlation coefficient (SRCC) of 0.97, and our method was also validated using actual acquired AO images. The experimental results indicate that our method is more accurate in evaluating AO image quality compared to traditional IQA methods.