Atmosphere Turbulence Blurred Image Restoration Based on Atmosphere Coherent Length

Changli, 温昌礼 Wen; Rong, 徐蓉 Xu; Tao, 门涛 Men; Changhai, 刘长海 Liu

doi:10.3788/aos201434.0301002

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To assess the viability of our proposed method, three classical NR-IQA methods, i.e., Tenengrad [ 25 ], LOG [ 24 ], and HyperIQA [ 17 ], were employed to compare our method’s effectiveness on both simulated and real data. The Tenengrad and LOG IQA methods are based on image NSS features and have been utilized in AO image assessment.…”

Section: Resultsmentioning

confidence: 99%

“…Tian et al proposed entropy measurement as an approach to assess AO images [ 23 ]. Guo et al used the normalized LOG domain [ 24 ], and Wen et al applied the Sobel approach to assess the quality of recovered images after undergoing a similar number of blind convolutions [ 25 ]. However, AO images based on ground-based telescopes not only undergo more complex degradation but also differ significantly from natural images in terms of the content: Since AO images are grayscale, color features cannot be used to assess them.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Gao,

Li,

Chen

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%