Every Moment Matters: Detail-Aware Networks to Bring a Blurry Image Alive

Motion-blurred images are the result of light accumulation over the period of camera exposure time, during which the camera and objects in the scene are in relative motion to each other. The inverse process of extracting an image sequence from a single motion-blurred image is an ill-posed vision problem. One key challenge is that the motions across frames are subtle, which makes the generating networks difficult to capture them and thus the recovery sequences lack motion details. In order to alleviate this problem, we propose a detail-aware network with three consecutive stages to improve the reconstruction quality by addressing specific aspects in the recovery process. The detail-aware network firstly models the dynamics using a cycle flow loss, resolving the temporal ambiguity of the reconstruction in the first stage. Then, a GramNet is proposed in the second stage to refine subtle motion between continuous frames using Gram matrices as motion representation. Finally, we introduce a HeptaGAN in the third stage to bridge the continuous and discrete nature of exposure time and recovered frames, respectively, in order to maintain rich detail. Experiments show that the proposed detail-aware networks produce sharp image sequences with rich details and subtle motion, outperforming the state-of-the-art methods.