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.

本研究提出了一种统一框架 DTNet，即一种面向方向和时间的双边学习网络，用于视频去莫尔纹。DTNet 在去莫尔纹的过程中有效地应用莫尔纹模式移除、对齐、色彩校正和细节修复等步骤，经过广泛实验证明，在峰值信噪比方面，该方法优于最先进的方法，并提供更好的视觉体验。