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.

翻译：在屏幕拍摄图像或视频时会产生摩尔纹，严重降低所拍摄图像或视频的质量。尽管近年来取得了进展，现有视频去摩尔纹方法忽略了摩尔纹的物理特性和形成过程，显著限制了视频恢复的效果。本文提出统一框架DTNet——一种面向视频去摩尔纹的方向感知与时序引导的双边学习网络。DTNet有效整合了摩尔纹去除、对齐、色彩校正和细节细化过程。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）以及色调与细节细化（TDR）。在FDDA中，我们采用多个方向DCT模式在频域执行摩尔纹去除，有效检测突出的摩尔纹边缘。随后，对去摩尔纹特征进行粗粒度和细粒度对齐，以促进相邻信息的利用。在TDR中，我们提出时序引导的双边学习流程，以减轻摩尔纹导致的色彩和细节退化，同时保留FDDA中恢复的频域信息。在FDDA对齐后的时序特征引导下，TDR学习用于恢复最终干净帧的仿射变换。大量实验表明，我们的视频去摩尔纹方法在PSNR上比现有最优方法高出2.3 dB，并能提供更优越的视觉体验。