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Camera shake during exposure is a major problem in hand-held photography, asit causes image blur that destroys details in the captured images.~In the realworld, such blur is mainly caused by both the camera motion and the complexscene structure.~While considerable existing approaches have been proposedbased on various assumptions regarding the scene structure or the cameramotion, few existing methods could handle the real 6 DoF camera motion.~In thispaper, we propose to jointly estimate the 6 DoF camera motion and remove thenon-uniform blur caused by camera motion by exploiting their underlyinggeometric relationships, with a single blurry image and its depth map (eitherdirect depth measurements, or a learned depth map) as input.~We formulate ourjoint deblurring and 6 DoF camera motion estimation as an energy minimizationproblem which is solved in an alternative manner. Our model enables therecovery of the 6 DoF camera motion and the latent clean image, which couldalso achieve the goal of generating a sharp sequence from a single blurryimage. Experiments on challenging real-world and synthetic datasets demonstratethat image blur from camera shake can be well addressed within our proposedframework.

Single Image Deblurring and Camera Motion Estimation with Depth Map