Moire patterns occur when capturing images or videos on screens, severely degrading the quality of the captured images or videos. Despite the recent progresses, existing video demoireing methods neglect the physical characteristics and formation process of moire patterns, significantly limiting the effectiveness of video recovery. This paper presents a unified framework, DTNet, a direction-aware and temporal-guided bilateral learning network for video demoireing. DTNet effectively incorporates the process of moire pattern removal, alignment, color correction, and detail refinement. Our proposed DTNet comprises two primary stages: Frame-level Direction-aware Demoireing and Alignment (FDDA) and Tone and Detail Refinement (TDR). In FDDA, we employ multiple directional DCT modes to perform the moire pattern removal process in the frequency domain, effectively detecting the prominent moire edges. Then, the coarse and fine-grained alignment is applied on the demoired features for facilitating the utilization of neighboring information. In TDR, we propose a temporal-guided bilateral learning pipeline to mitigate the degradation of color and details caused by the moire patterns while preserving the restored frequency information in FDDA. Guided by the aligned temporal features from FDDA, the affine transformations for the recovery of the ultimate clean frames are learned in TDR. Extensive experiments demonstrate that our video demoireing method outperforms state-of-the-art approaches by 2.3 dB in PSNR, and also delivers a superior visual experience. Our code is available at https://github.com/rebeccaeexu/DTNet.

翻译：在屏幕上拍摄图像或视频时，摩尔纹图案会出现，严重降低所捕获图像或视频的质量。尽管近期取得了进展，现有视频去摩尔纹方法忽视了摩尔纹图案的物理特性和形成过程，极大限制了视频恢复的效果。本文提出一个统一框架DTNet，这是一种方向感知和时序引导的双边学习网络，用于视频去摩尔纹。DTNet有效集成了摩尔纹图案去除、对齐、色彩校正和细节精化等过程。我们提出的DTNet包含两个主要阶段：帧级方向感知去摩尔纹与对齐（FDDA）以及色调与细节精化（TDR）。在FDDA阶段，我们采用多个方向DCT模式在频域中执行摩尔纹图案去除过程，有效检测出显著的摩尔纹边缘。然后，在去摩尔纹特征上应用粗粒度和细粒度对齐，以促进相邻信息的利用。在TDR阶段，我们提出一种时序引导的双边学习流水线，以减轻摩尔纹图案导致的色彩和细节退化，同时保留FDDA阶段恢复的频域信息。在FDDA阶段对齐的时序特征引导下，TDR阶段学习用于恢复最终清洁帧的仿射变换。大量实验表明，我们的视频去摩尔纹方法在PSNR上比现有最先进方法高出2.3 dB，并带来更优越的视觉体验。我们的代码已开源在https://github.com/rebeccaeexu/DTNet。