Name
Abstract | ||
|---|---|---|
Blind image deblurring is a challenging problem in computer vision, which aims to restore both the blur kernel and the latent sharp image from only a blurry observation. Inspired by the prevalent self-example prior in image super-resolution, in this paper, we observe that a coarse enough image down-sampled from a blurry observation is approximately a low-resolution version of the latent sharp image. We prove this phenomenon theoretically and define the coarse enough image as a latent structure prior of the unknown sharp image. Starting from this prior, we propose to restore sharp images from the coarsest scale to the finest scale on a blurry image pyramid, and progressively update the prior image using the newly restored sharp image. These coarse-to-fine priors are referred to as \textit{Multi-Scale Latent Structures} (MSLS). Leveraging the MSLS prior, our algorithm comprises two phases: 1) we first preliminarily restore sharp images in the coarse scales; 2) we then apply a refinement process in the finest scale to obtain the final deblurred image. In each scale, to achieve lower computational complexity, we alternately perform a sharp image reconstruction with fast local self-example matching, an accelerated kernel estimation with error compensation, and a fast non-blind image deblurring, instead of computing any computationally expensive non-convex priors. We further extend the proposed algorithm to solve more challenging non-uniform blind image deblurring problem. Extensive experiments demonstrate that our algorithm achieves competitive results against the state-of-the-art methods with much faster running speed. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tcsvt.2019.2919159 | IEEE Transactions on Circuits and Systems for Video Technology |
DocType | Volume | Citations |
Journal | abs/1906.04442 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanchao Bai | 1 | 34 | 4.36 |
| Hui-zhu Jia | 2 | 96 | 20.45 |
| Ming Jiang | 3 | 1 | 0.35 |
| Xianming Liu | 4 | 461 | 47.55 |
| Xiaodong Xie | 5 | 139 | 30.45 |
| Wen Gao | 6 | 11374 | 741.77 |