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。

时序引导的双边学习方向感知视频去蒙皮

Direction-aware Video Demoireing with Temporal-guided Bilateral Learning

Moire patterns appear frequently when taking photos of digital screens,
drastically degrading the image quality. Despite the advance of CNNs in image
demoireing, existing networks are with heavy design, causing redundant
computation burden for mobile devices. In this paper, we launch the first study
on accelerating demoireing networks and propose a dynamic demoireing
acceleration method (DDA) towards a real-time deployment on mobile devices. Our
stimulus stems from a simple-yet-universal fact that moire patterns often
unbalancedly distribute across an image. Consequently, excessive computation is
wasted upon non-moire areas. Therefore, we reallocate computation costs in
proportion to the complexity of image patches. In order to achieve this aim, we
measure the complexity of an image patch by designing a novel moire prior that
considers both colorfulness and frequency information of moire patterns. Then,
we restore image patches with higher-complexity using larger networks and the
ones with lower-complexity are assigned with smaller networks to relieve the
computation burden. At last, we train all networks in a parameter-shared
supernet paradigm to avoid additional parameter burden. Extensive experiments
on several benchmarks demonstrate the efficacy of our proposed DDA. In
addition, the acceleration evaluated on the VIVO X80 Pro smartphone equipped
with a chip of Snapdragon 8 Gen 1 shows that our method can drastically reduce
the inference time, leading to a real-time image demoireing on mobile devices.
Source codes and models are released at this https URL

采用动态加速方法 (DDA) 和新设计的 moire prior 对漫反射现象进行实时检测和优化，以在移动设备上实现拍照质量的提高，并通过 Snapdragon 8 Gen 1 芯片在 Vivo X80 Pro 智能手机上验证了其在推理时间上的显著改善。

移动设备实时图像去莫尔滤波演示

Real-Time Image Demoireing on Mobile Devices

When smartphone cameras are used to take photos of digital screens, usually
moire patterns result, severely degrading photo quality. In this paper, we
design a wavelet-based dual-branch network (WDNet) with a spatial attention
mechanism for image demoireing. Existing image restoration methods working in
the RGB domain have difficulty in distinguishing moire patterns from true scene
texture. Unlike these methods, our network removes moire patterns in the
wavelet domain to separate the frequencies of moire patterns from the image
content. The network combines dense convolution modules and dilated convolution
modules supporting large receptive fields. Extensive experiments demonstrate
the effectiveness of our method, and we further show that WDNet generalizes to
removing moire artifacts on non-screen images. Although designed for image
demoireing, WDNet has been applied to two other low-levelvision tasks,
outperforming state-of-the-art image deraining and derain-drop methods on the
Rain100h and Raindrop800 data sets, respectively.

本文设计了基于小波变换和空间注意力机制的双分支网络（WDNet），用于图像去马赛克处理，并在 Rain 100 和 Raindrop 800 数据集上优于现有的图像去雨滴方法。