https://scholars.lib.ntu.edu.tw/handle/123456789/580965
標題: | Deep Priors Inside an Unrolled and Adaptive Deconvolution Model | 作者: | Ko H.-C Chang J.-Y JIAN-JIUN DING |
關鍵字: | Computer vision; Image enhancement; Restoration; Adaptive deconvolution; High frequency components; Ill posed problem; Image de convolutions; Learning-based methods; Parameter adjustments; Proposed architectures; Restoration quality; Image reconstruction | 公開日期: | 2021 | 卷: | 12623 LNCS | 起(迄)頁: | 371-388 | 來源出版物: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 摘要: | Image deconvolution is an essential but ill-posed problem even if the degradation kernel is known. Recently, learning based methods have demonstrated superior image restoration quality in comparison to traditional methods which are typically based on empirical statistics and parameter adjustment. Though coming up with outstanding performance, most of the plug-and-play priors are trained in a specific degradation model, leading to inferior performance on restoring high-frequency components. To address this problem, a deblurring architecture that adopts (1) adaptive deconvolution modules and (2) learning based image prior solvers is proposed. The adaptive deconvolution module adjusts the regularization weight locally to well process both smooth and non-smooth regions. Moreover, a cascade made of image priors is learned from the mapping between intermediates thus robust to arbitrary noise, aliasing, and artifact. According to our analysis, the proposed architecture can achieve a significant improvement on the convergence rate and result in an even better restoration performance. ? 2021, Springer Nature Switzerland AG. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103303900&doi=10.1007%2f978-3-030-69532-3_23&partnerID=40&md5=7fa2bc9e84f526b99e4a4bff93bc3c91 https://scholars.lib.ntu.edu.tw/handle/123456789/580965 |
ISSN: | 03029743 | DOI: | 10.1007/978-3-030-69532-3_23 |
顯示於: | 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。