Solar Speckle Image Deblurring With Deep Prior Constraint Based on Regularization | |
Jin, Yahui1; Jiang, Murong1; Yang L(杨磊)2; Zou SZ(邹思仲)2; Deng, Linhao1; Chen JY(谌俊毅)2 | |
刊名 | IEEE ACCESS |
2022 | |
卷号 | 10页码:128195-128206 |
关键词 | Solar speckle image regularization model deep image prior point spread function |
ISSN号 | 2169-3536 |
DOI | 10.1109/ACCESS.2022.3226812 |
产权排序 | 第2完成单位 |
文献子类 | Article |
英文摘要 | The solar speckle image has the characteristics with single features, more noise, and blurred local details. Most of the existing deep learning deblurring methods for solar speckle images have some problems, such as high-frequency loss, artifact generation, and dependence on the paired image. In this paper, a deep prior deblurring method fusing the regularization model and prior constraint network is proposed. Firstly, the traditional handcrafted regularization priors are added to the network parameterized blind deconvolution model. The image gradient prior and blur kernel initial parameters are respectively used to the network parameterization process of two variables in the blind deconvolution model, which are the latent clean image variables and blur kernel variables. After that, the solar speckle image deep prior deblurring model is established. Secondly, the blur kernel generation network input is estimated by using the atmospheric point spread function (PSF) to improve the model convergence speed. Thirdly, a latent clean image generation network including joint gradient branching and Feature Pyramid Network (FPN) structure is designed to enhance image local edge details. Finally, a joint loss function including pixel loss, image prior loss, and mean squared error (MSE) loss is introduced to guide the model for alternate training. It can obtain the best parameter values of latent clean image and blur kernel, and achieve the solar speckle image high-resolution reconstruction. The experimental results show that the proposed method can eliminate the dependence on the reference image, and the reconstructed image has less noise and more obvious high-frequency details, faster network convergence, and two evaluation indicators of Peak Signal Noise Ratio (PSNR) and Structural Similarity (SSIM) are significantly improved. |
学科主题 | 天文学 ; 太阳与太阳系 ; 太阳与太阳系其他学科 ; 计算机科学技术 ; 计算机应用 ; 计算机图象处理 |
URL标识 | 查看原文 |
出版地 | 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA |
资助项目 | N/A |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000899129300001 |
资助机构 | N/A |
内容类型 | 期刊论文 |
版本 | 出版稿 |
源URL | [http://ir.ynao.ac.cn/handle/114a53/25698] |
专题 | 云南天文台_抚仙湖太阳观测站 |
通讯作者 | Jiang, Murong |
作者单位 | 1.School of Information Science and Engineering, Yunnan University, Kunming, China; 2.Yunnan Observatories, Chinese Academy of Sciences, Kunming, China; |
推荐引用方式 GB/T 7714 | Jin, Yahui,Jiang, Murong,Yang L,et al. Solar Speckle Image Deblurring With Deep Prior Constraint Based on Regularization[J]. IEEE ACCESS,2022,10:128195-128206. |
APA | Jin, Yahui,Jiang, Murong,Yang L,Zou SZ,Deng, Linhao,&Chen JY.(2022).Solar Speckle Image Deblurring With Deep Prior Constraint Based on Regularization.IEEE ACCESS,10,128195-128206. |
MLA | Jin, Yahui,et al."Solar Speckle Image Deblurring With Deep Prior Constraint Based on Regularization".IEEE ACCESS 10(2022):128195-128206. |
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