5 accepted papers
Despite recent progress, diffusion-based video frame interpolation methods still struggle with large, complex motions, resulting in discontinuous motions and inconsistent object appearances across frames. We observe that these limitations arise from both the current full-sequence interpolation strat
Recent research on video Frame Interpolation (VFI) shows that a pretrained Video Diffusion Model (VDM) can solve many challenging scenarios, including large or complex motion. However, VDMs require tedious diffusion sampling, making the inference slow. One possible way to accelerate is to distill a…
Joint filtering is a fundamental problem in computer vision with applications in many different areas. Most existing algorithms solve this problem with a weighted averaging process to aggregate input pixels. However, the weight matrix of this process is often empirically designed and not robust to c…
We propose an end-to-end trainable Convolutional Neural Network (CNN), named GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules: pre-processing, backbone, and post-processing. The trainable pre-processing module can generate learned inputs with better diversity and…
Most existing super-resolution methods do not perform well in real scenarios due to lack of realistic training data and information loss of the model input. To solve the first problem, we propose a new pipeline to generate realistic training data by simulating the imaging process of digital cameras.…