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.
As depth can significantly simplify the deblurring problem, the multi-view methods have been proposed to leverage on depth information. Building upon the work of Ezra and Nayar @cite_32 , Li al @cite_33 extended the hybrid camera with an additional low-resolution video camera where two low-resolution cameras form a stereo pair and provide a low-resolution depth map. Tai al @cite_0 used a hybrid camera system to compute a pixel-wise kernel with optical flow. Xu al @cite_21 inferred depth from two blur images captured by a stereo camera and proposed a hierarchical estimation framework to remove motion blur caused by in-plane translation. Just recently, Sellent al @cite_28 proposed a video deblurring technique based on stereo video, where 3D scene flow is estimated from blur images using a piecewise rigid 3D scene flow representation.
@cite_33 @cite_28 @cite_21 @cite_32 @cite_0
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We present a hybrid camera that combines the advantages of a high resolution camera and a high speed camera. Our hybrid camera consists of a pair of low-resolution high-speed (LRHS) cameras and a single high-resolution low-speed (HRLS) camera. The LRHS cameras are able to capture fast-motion with little motion blur. They also form a stereo pair and provide a low-resolution depth map. The HRLS camera provides a high spatial resolution but also introduces severe motion blur when capturing fast moving objects. We develop efficient algorithms to simultaneously motion-deblur the HRLS image and reconstruct a high resolution depth map. Our method estimates the motion flow in the LRHS pair and then warps the flow field to the HRLS camera to estimate the point spread function (PSF). We then deblur the HRLS image and use the resulting image to enhance the low-resolution depth map using joint bilateral filters. We demonstrate the hybrid camera in depth map super-resolution and motion deblurring with spatially varying kernels. Experiments show that our framework is robust and highly effective. State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a new video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as moving objects, camera shake, depth variation, and defocus, we estimate pixel-wise varying non-uniform blur kernels. We infer bidirectional optical flows to handle motion blurs, and also estimate Gaussian blur maps to remove optical blur from defocus. Therefore, we propose a single energy model that jointly estimates optical flows, defocus blur maps and latent frames. We also provide a framework and efficient solvers to minimize the proposed energy model. By optimizing the energy model, we achieve significant improvements in removing general blurs, estimating optical flows, and extending depth-of-field in blurry frames. Moreover, in this work, to evaluate the performance of non-uniform deblurring methods objectively, we have constructed a new realistic dataset with ground truths. In addition, extensive experimental results on publicly available challenging videos demonstrate that the proposed method produces qualitatively superior performance than the state-of-the-art methods which often fail in either deblurring or optical flow estimation. Motion blur frequently occurs in dense 3D reconstruction using a single moving camera, and it degrades the quality of the 3D reconstruction. To handle motion blur caused by rapid camera shakes, we propose a blur-aware depth reconstruction method, which utilizes a pixel correspondence that is obtained by considering the effect of motion blur. Motion blur is dependent on 3D geometry, thus parameter zing blurred appearance of images with scene depth given camera motion is possible and a depth map can be accurately estimated from the blur-considered pixel correspondence. The estimated depth is then converted into pixel-wise blur kernels, and non-uniform motion blur is easily removed with low computational cost. The obtained blur kernel is depth-dependent, thus it effectively addresses scene-depth variation, which is a challenging problem in conventional non-uniform deblurring methods. We propose a novel approach to reduce spatially varying motion blur using a hybrid camera system that simultaneously captures high-resolution video at a low-frame rate together with low-resolution video at a high-frame rate. Our work is inspired by Ben-Ezra and Nayar who introduced the hybrid camera idea for correcting global motion blur for a single still image. We broaden the scope of the problem to address spatially varying blur as well as video imagery. We also reformulate the correction process to use more information available in the hybrid camera system, as well as iteratively refine spatially varying motion extracted from the low-resolution high-speed camera. We demonstrate that our approach achieves superior results over existing work and can be extended to deblurring of moving objects.