FPANet: Frequency-based video demoiréing using frame-level post alignment

被引：0

作者：

Oh, Gyeongrok ^{[1
]}

Kim, Sungjune ^{[1
]}

Gu, Heon ^{[4
]}

Yoon, Sang Ho ^{[3
]}

Kim, Jinkyu ^{[2
]}

Kim, Sangpil ^{[1
]}

机构：

[1] Department of Artificial Intelligence, Korea University, Korea, Republic of

[2] Department of Computer Science and Engineering, Korea University, Korea, Republic of

[3] of Culture Technology, KAIST, Korea, Republic of

[4] LG Display Research Center, Korea, Republic of

来源：

Neural Networks | 2025年 / 184卷

关键词：

Image quality - Image reconstruction - Large datasets - Video analysis;

D O I：

10.1016/j.neunet.2024.107021

中图分类号：

学科分类号：

摘要：

Moiré patterns, created by the interference between overlapping grid patterns in the pixel space, degrade the visual quality of images and videos. Therefore, removing such patterns (demoiréing) is crucial, yet remains a challenge due to their complexities in sizes and distortions. Conventional methods mainly tackle this task by only exploiting the spatial domain of the input images, limiting their capabilities in removing large-scale moiré patterns. Therefore, this work proposes FPANet, an image–video demoiréing network that learns filters in both frequency and spatial domains, improving the restoration quality by removing various sizes of moiré patterns. To further enhance, our model takes multiple consecutive frames, learning to extract frame-invariant content features and outputting better quality temporally consistent images. We demonstrate the effectiveness of our proposed method with a publicly available large-scale dataset, observing that ours outperforms the state-of-the-art approaches in terms of image and video quality metrics and visual experience. © 2024

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