The conventional methods for estimating camera poses and scene structures from severely blurry or low resolution images often result in failure. The off-the-shelf deblurring or super-resolution methods may show visually pleasing results. However, applying each technique independently before matching is generally unprofitable because this naive series of procedures ignores the consistency between images. In this paper, we propose a pioneering unified framework that solves four problems simultaneously, namely, dense depth reconstruction, camera pose estimation, super-resolution, and deblurring. By reflecting a physical imaging process, we formulate a cost minimization problem and solve it using an alternating optimization technique. The experimental results on both synthetic and real videos show high-quality depth maps derived from severely degraded images that contrast the failures of naive multi-view stereo methods. Our proposed method also produces outstanding deblurred and super-resolved images unlike the independent application or combination of conventional video deblurring, super-resolution methods.
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. Scene depth variation is an important factor that leads to spatially-varying camera motion blur. Most of the previous methods require auxiliary cameras or user interaction to make depth-aware deblurring tractable. In this work, we propose to use a noisy blurred noisy image sequence and simultaneously recorded inertial measurements to jointly estimate scene depth and remove spatially-varying blur caused by depth variation and camera in-plane motion. The inertial data could provide initialization of camera motion parameters, while the noisy image pair preserve large-scale sharp edges from which a coarse disparity map can be generated. However, this initial estimate is not accurate enough to produce a high-quality clean image. Therefore, we develop an optimization scheme to refine depth, motion parameters and latent image alternately. A Markov Random Field (MRF) framework is formulated to solve for the depth map by exploiting both stereo cues and motion blur cues and the residual Richardson-Lucy algorithm is used to effectively suppress deconvolution ringing artifacts. Experimental results demonstrate that our approach can address both depth estimation as well as image deblurring.