Name
Abstract | ||
|---|---|---|
Most state-of-the-art dynamic scene deblurring methods based on accurate motion segmentation assume that motion blur is small or that the specific type of motion causing the blur is known. In this paper, we study a motion segmentation-free dynamic scene deblurring method, which is unlike other conventional methods. When the motion can be approximated to linear motion that is locally (pixel-wise) varying, we can handle various types of blur caused by camera shake, including out-of-plane motion, depth variation, radial distortion, and so on. Thus, we propose a new energy model simultaneously estimating motion flow and the latent image based on robust total variation (TV)-L1 model. This approach is necessary to handle abrupt changes in motion without segmentation. Furthermore, we address the problem of the traditional coarse-to-fine deblurring framework, which gives rise to artifacts when restoring small structures with distinct motion. We thus propose a novel kernel re-initialization method which reduces the error of motion flow propagated from a coarser level. Moreover, a highly effective convex optimization-based solution mitigating the computational difficulties of the TV-L1 model is established. Comparative experimental results on challenging real blurry images demonstrate the efficiency of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CVPR.2014.348 | CVPR |
Keywords | Field | DocType |
kernel re-initialization method,motion flow estimation,segmentation-free dynamic scene deblurring method,radial distortion,computational difficulties mitigation,image segmentation,linear motion,pixel-wise variation,blurry images,coarse-to-fine deblurring frame-work,convex programming,image restoration,motion estimation,energy model,motion segmentation,convex optimization-based solution,out-of-plane motion,tv-l1,small structures restoration,depth variation,total variation model,motion error reduction,dynamics,kernel,vectors | Structure from motion,Computer vision,Linear motion,Motion field,Quarter-pixel motion,Deblurring,Computer science,Segmentation,Motion blur,Artificial intelligence,Motion estimation | Conference |
ISSN | Citations | PageRank |
1063-6919 | 15 | 0.73 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae Hyun Kim | 1 | 359 | 29.05 |
| Kyoung Mu Lee | 2 | 3228 | 153.84 |