Motion estimation method for blurred videos and application of deblurring with spatially varying blur kernels

He, Xiangjian; Luo, Ting; Yuk, S. C.; Chow, Ka-Ho; Wong, Kwan-Yee K.; Chung, Ronald

doi:10.1109/iccit.2010.5711083

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…The presence of blurring features in the scene easily fails the traditional optical flow methods because of the violation to brightness constancy assumption. Only a few of approaches are introduced to settle this problem [52,53,54,55,56]. Portz et al [52] treat the appearance of each input frame as a parameterized function combining pixel motion and blur motion.…”

Section: Optical Flowmentioning

confidence: 99%

Learn to model blurry motion via directional similarity and filtering

Chen

et al. 2018

Pattern Recognition

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It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the camera-shake footages. The proposed CNNs are then integrated into an iterative optical flow framework, which enable the capability of modeling and solving both the blind deconvolution and the optical flow estimation problems simultaneously. Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches

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Section: Optical Flowmentioning

confidence: 99%

Learn to model blurry motion via directional similarity and filtering

Chen

et al. 2018

Pattern Recognition

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“…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

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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.

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“…He et al [10] perform motion estimation for spatiallyvarying blur by detecting corners and using hierarchical block matching to obtain flows for the corners. Flows for other pixels are obtained by interpolating between the available flows in a sparse-to-dense approach.…”

Section: Related Workmentioning

confidence: 99%

Optical flow in the presence of spatially-varying motion blur

Portz

Zhang

Jiang

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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This paper extends the classical warping-based optical flow method to achieve accurate flow in the presence of spatially-varying motion blur. Our idea is to parameterize the appearance of each frame as a function of both the pixel motion and the motion-induced blur. We search for the flows that best match two consecutive frames, which amounts to finding the derivative of a blurred frame with respect to both the motion and the blur, where the blur itself is a function of the motion. We propose an efficient technique to calculate the derivatives using pre-filtering. Our technique avoids performing spatially-varying filtering (which can be computationally expensive) during the optimization iterations. In the end, our derivative calculation technique can be easily incorporated with classical flow code to handle video with non-uniform motion blur with little performance penalty. Our method is evaluated on both synthetic and real videos and outperforms conventional flow methods in the presence of motion blur.

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Motion estimation method for blurred videos and application of deblurring with spatially varying blur kernels

Cited by 10 publications

References 16 publications

Learn to model blurry motion via directional similarity and filtering

Learn to model blurry motion via directional similarity and filtering

Stereo Video Deblurring

Optical flow in the presence of spatially-varying motion blur

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