Demoiréing is the task of removing moiré patterns, which are commonly caused by the interference between the screen and digital cameras. Although research on single image demoiréing has made great progress, research on video demoiréing has received less attention from the community. Video demoiréing poses a new set of challenges. First, most existing video restoration algorithms rely on multi-resolution pixel-based alignment, which can cause damage to the details of the predicted results. Second, these algorithms are based on flow-based loss or relation-based loss, making it difficult to handle the large motions of adjacent frames while keeping temporal consistency intact. To address these challenges, we present a novel deep learning-based approach called the Deep Temporal Color Embedding network (DTCENet) that employs an invertible network to align distortion color patches in a patch-based embedding framework. DTCENet can well preserve details while eliminate color distortions. Furthermore, we introduce a video-image invertible loss function to effectively handle the color inconsistent problem of adjacent frames. Our approach shows promising results in demoiréing videos, with improved performance over existing state-of-the-art algorithms. Our method gets about 10% improvements in terms of LPIPS and 10.3% improvements in terms of FID compared with the recent SOTA methods.

Low-light video enhancement (LLVE) is an important yet challenging task with many applications such as photographing and autonomous driving. Unlike single image low-light enhancement, most LLVE methods utilize temporal information from adjacent frames to restore the color and remove the noise of the target frame. However, these algorithms, based on the framework of multi-frame alignment and enhancement, may produce multi-frame fusion artifacts when encountering extreme low light or fast motion. In this paper, inspired by the low latency and high dynamic range of events, we use synthetic events from multiple frames to guide the enhancement and restoration of low-light videos. Our method contains three stages: 1) event synthesis and enhancement, 2) event and image fusion, and 3) low-light enhancement. In this framework, we design two novel modules (event-image fusion transform and event-guided dual branch) for the second and third stages, respectively. Extensive experiments show that our method outperforms existing low-light video or single image enhancement approaches on both synthetic and real LLVE datasets. Our code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/LLVE-SEG.