.png){kind=icon}
返回
Spatial-scale-regularized blur kernel estimation for blind image deblurring
.png){kind=icon}
PRE
AI
DOI：10.1016/j.image.2018.07.010.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Blind image deblurring is a long-standing and challenging inverse problem in image processing. In this paper, we propose a new spatial-scale-regularized approach to estimate a blur kernel (BK) from a single motion blurred image by regularizing the spatial scale sizes of image edges. Furthermore, by applying shock filter into the proposed model, our method is able to recover sharp large-scale edges for accurate BK estimation. Finally, we propose an efficient optimization strategy which can solve the proposed model efficiently. Extensive experiments compared with state-of-the-art blind motion deblurring methods demonstrate the effectiveness of the proposed method in terms of subjective vision, deconvolution error ratio (DER), peak signal-to-noise ratio (PSNR), self-similarity measure (SSIM), and sum of squared differences error (SSDE).
Keyword:
Blind image deblurring
Spatial scale
Shock filter
Large-scale edges
Blur kernel
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
S
IF:
2.7
论文数:
2.8K
被引数:
4.2K
.png){kind=icon}
查看更多内容