Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing
attention in the computer vision community. A challenging benchmark named REDS is …

Despite deep end-to-end learning methods have shown their superiority in removing non-
uniform motion blur, there still exist major challenges with the current multi-scale and scale …

Video restoration concerns the recovery of a clean video sequence starting from its
degraded version. Different video restoration tasks exist, including denoising, deblurring …

Blind deconvolution is a classical yet challenging low-level vision problem with many real-
world applications. Traditional maximum a posterior (MAP) based methods rely heavily on …

Recently, deep learning based point cloud descriptors have achieved impressive results in
the place recognition task. Nonetheless, due to the sparsity of point clouds, how to extract …

Event-based cameras can measure intensity changes (called'events') with microsecond
accuracy under high-speed motion and challenging lighting conditions. With the active pixel …

Point cloud based retrieval for place recognition is an emerging problem in vision field. The
main challenge is how to find an efficient way to encode the local features into a …

Video deblurring is a challenging task due to the spatially variant blur caused by camera
shake, object motions, and depth variations, etc. Existing methods usually estimate optical …

Recovering sharp video sequence from a motion-blurred image is highly ill-posed due to the
significant loss of motion information in the blurring process. For event-based cameras …

Integrating Stereo Imaging technology into medical diagnostics and surgeries marks a
significant revolution in medical sciences. This advancement gives surgeons and physicians …