Video-based non-uniform object motion blur estimation and deblurring

Deng, Xiaoyu; Shen, Yi; Song, Mingli; Tao, Dacheng; Bu, Jiajun; Chen, Chun

doi:10.1016/j.neucom.2012.01.017

Cited by 22 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since blur parameters and the latent image are difficult to be estimated from a single image, the monocular based approaches are extended to video to remove blurs in dynamic scenes [32,37]. To this end, Deng et al [4] and He et al [11] apply feature tracking of a single moving object to obtain 2D displacement-based blur kernels for deblurring. Matsushita et al [24] and Cho et al [3] proposed to exploit the existence of salient sharp frames in videos.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Stereo Video Deblurring and Scene Flow Estimation

Pan

Dai

Liu

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

Videos for outdoor scene often show unpleasant blur effects due to the large relative motion between the camera and the dynamic objects and large depth variations. Existing works typically focus monocular video deblurring. In this paper, we propose a novel approach to deblurring from stereo videos. In particular, we exploit the piece-wise planar assumption about the scene and leverage the scene flow information to deblur the image. Unlike the existing approach [31] which used a pre-computed scene flow, we propose a single framework to jointly estimate the scene flow and deblur the image, where the motion cues from scene flow estimation and blur information could reinforce each other, and produce superior results than the conventional scene flow estimation or stereo deblurring methods. We evaluate our method extensively on two available datasets and achieve significant improvement in flow estimation and removing the blur effect over the state-of-the-art methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Simultaneous Stereo Video Deblurring and Scene Flow Estimation

Pan

Dai

Liu

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

show abstract

“…Approximation errors (c) are also present close to the rotation axis where motions are small (extremly large yaw angle and all intensities scaled for better visibility) era. He et al [32] and Deng et al [33] apply feature tracking of a single moving object to obtain 2D displacement-based blur kernels for deblurring. Wulff and Black [18] refine the latter approach and perform segmentation into two layers, estimation of the affine motion parameters, as well as deblurring of each layer jointly.…”

Section: Fig 2 Stereo Video Deblurringmentioning

confidence: 99%

Stereo Video Deblurring

Sellent

Rother

Roth

2016

Computer Vision – ECCV 2016

View full text Add to dashboard Cite

Abstract. Videos acquired in low-light conditions often exhibit motion blur, which depends on the motion of the objects relative to the camera. This is not only visually unpleasing, but can hamper further processing. With this paper we are the first to show how the availability of stereo video can aid the challenging video deblurring task. We leverage 3D scene flow, which can be estimated robustly even under adverse conditions. We go beyond simply determining the object motion in two ways: First, we show how a piecewise rigid 3D scene flow representation allows to induce accurate blur kernels via local homographies. Second, we exploit the estimated motion boundaries of the 3D scene flow to mitigate ringing artifacts using an iterative weighting scheme. Being aware of 3D object motion, our approach can deal robustly with an arbitrary number of independently moving objects. We demonstrate its benefit over state-of-the-art video deblurring using quantitative and qualitative experiments on rendered scenes and real videos.

show abstract

“…Tai et al [26] and Ben-Ezra and Nayar [27] took advantage of a hybrid camera system, and estimated the blur kernel of the high resolution but blurred frame by the optical flows of the corresponding low resolution frames. In the video deblurring problem, Yamaguchi et al [28] and Deng et al [29] estimated the PSF from the optical flows between image sequences. In [30], a registration based deblurring approach has been proposed from two spatially variant blurred images.…”

Section: Combinationmentioning

confidence: 99%