Learning to Extract a Video Sequence from a Single Motion-Blurred Image

We present a method to extract a video sequence from a single motion-blurred image. Motion-blurred images are the result of an averaging process, where instant frames are accumulated over time during the exposure of the sensor. Unfortunately, reversing this process is nontrivial. Firstly, averaging destroys the temporal ordering of the frames. Secondly, the recovery of a single frame is a blind deconvolution task, which is highly ill-posed. We present a deep learning scheme that gradually reconstructs a temporal ordering by sequentially extracting pairs of frames. Our main contribution is to introduce loss functions invariant to the temporal order. This lets a neural network choose during training what frame to output among the possible combinations. We also address the ill-posedness of deblurring by designing a network with a large receptive field and implemented via resampling to achieve a higher computational efficiency. Our proposed method can successfully retrieve sharp image sequences from a single motion blurred image and can generalize well on synthetic and real datasets captured with different cameras

Multi-View Image Compositions

The geometry of single-viewpoint panoramas is well understood: multiple pictures taken from the same viewpoint may be stitched together into a consistent panorama mosaic. By contrast, when the point of view changes or when the scene changes (e.g., due to objects moving) no consistent mosaic may be obtained, unless the structure of the scene is very special. Artists have explored this problem and demonstrated that geometrical consistency is not the only criterion for success: incorporating multiple view points in space and time into the same panorama may produce compelling and informative pictures. We explore this avenue and suggest an approach to automating the construction of mosaics from images taken from multiple view points into a single panorama. Rather than looking at 3D scene consistency we look at image consistency. Our approach is based on optimizing a cost function that keeps into account image-to-image consistency which is measured on point-features and along picture boundaries. The optimization explicitly considers occlusion between pictures. We illustrate our ideas with a number of experiments on collections of images of objects and outdoor scenes