Frequency Domain Based Optimization for Image Demoireing
Authors: 
Yingcong Huang, Qingyu Mao, Jiacong Chen, Yuqing Zhao, Fanyang Meng, Yongsheng LiangAuthors Info & Claims
Article No.: 12, Pages 1 - 7
Abstract
Image demoiréing is a corrective procedure aiming to eliminate the color-distorting moiré patterns when taking photos of digital screens. Currently, existing demoiréing methods typically rely on spatial domain loss optimization. However, moiré patterns at high frequencies cannot be effectively removed by spatial domain optimization. To further improve the restoration, we explore the potential impact of frequency domain optimization, which has not yet received extensive attention in this field. The frequency domain contains more detailed information about the image, particularly in higher frequencies. To this end, complementing the existing image-space loss functions, the implementation of frequency-space optimization enhances the performance of demoiréing networks by prioritizing frequency consistency. Our method is generic and can help classic image demoiréing models regain competitive performance. Extensive qualitative and quantitative experiments were conducted to rigorously evaluate the efficacy of our method. For instance, our method achieves an unprecedented performance of 45.3325 PSNR on LCD-Moiré datasets,which is an improvement of +1.6 percent points over the previous state of the art.
References
[1]
Mu Cai, Hong Zhang, Huijuan Huang, Qichuan Geng, Yixuan Li, and Gao Huang. Frequency domain image translation: More photo-realistic, better identity-preserving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13930–13940, 2021.
[2]
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, pages 213–229. Springer, 2020.
[3]
Xiongli Chai, Feng Shao, Qiuping Jiang, and Yo-Sung Ho. Roundness- preserving warping for aesthetic enhancement-based stereoscopic image editing. IEEE Transactions on Circuits and Systems for Video Technol- ogy, 31(4):1463–1477, 2020.
[4]
Xingyu Chen, Junzhi Yu, Shihan Kong, Zhengxing Wu, and Li Wen. Joint anchor-feature refinement for real-time accurate object detection in images and videos. IEEE Transactions on Circuits and Systems for Video Technology, 31(2):594–607, 2020.
[5]
Xi Cheng, Zhenyong Fu, and Jian Yang. Multi-scale dynamic feature en- coding network for image demoire´ing. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pages 3486–3493. IEEE, 2019.
[6]
Bin He, Ce Wang, Boxin Shi, and Ling-Yu Duan. Fhde 2 net: Full high definition demoireing network. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16, pages 713–729. Springer, 2020.
[7]
Liming Jiang, Bo Dai, Wayne Wu, and Chen Change Loy. Focal frequency loss for image reconstruction and synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13919–13929, 2021.
[8]
Fanglei Liu, Jingyu Yang, and Huanjing Yue. Moire´ pattern removal from texture images via low-rank and sparse matrix decomposition. In 2015 Visual Communications and Image Processing (VCIP), pages 1–4. IEEE, 2015.
[9]
Pengju Liu, Hongzhi Zhang, Kai Zhang, Liang Lin, and Wangmeng Zuo. Multi-level wavelet-cnn for image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 773–782, 2018.
[10]
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville. On the spectral bias of neural networks. In International Conference on Machine Learning, pages 5301–5310. PMLR, 2019.
[11]
Yujing Sun, Yizhou Yu, and Wenping Wang. Moire´ photo restoration using multiresolution convolutional neural networks. IEEE Transactions on Image Processing, 27(8):4160–4172, 2018.
[12]
Mingxing Tan, Ruoming Pang, and Quoc V Le. Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pages 10781–10790, 2020.
[13]
Akash Tayal, Jivansha Gupta, Arun Solanki, Khyati Bisht, Anand Nay- yar, and Mehedi Masud. Dl-cnn-based approach with image processing techniques for diagnosis of retinal diseases. Multimedia Systems, pages 1–22, 2021.
[14]
Zhouping Wei, Jian Wang, Helen Nichol, Sheldon Wiebe, and Dean Chapman. A median-gaussian filtering framework for moire´ pattern noise removal from x-ray microscopy image. Micron, 43(2-3):170–176, 2012.
[15]
Shengke Xue, Wenyuan Qiu, Fan Liu, and Xinyu Jin. Faster image super-resolution by improved frequency-domain neural networks. Sig- nal, Image and Video Processing, 14:257–265, 2020.
[16]
Shanghui Yang, Yajing Lei, Shuangyu Xiong, and Wei Wang. High resolution demoire network. In 2020 IEEE International Conference on Image Processing (ICIP), pages 888–892. IEEE, 2020.
[17]
Hu Yu, Naishan Zheng, Man Zhou, Jie Huang, Zeyu Xiao, and Feng Zhao. Frequency and spatial dual guidance for image dehazing. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIX, pages 181–198. Springer, 2022.
[18]
Xin Yu, Peng Dai, Wenbo Li, Lan Ma, Jiajun Shen, Jia Li, and Xiaojuan Qi. Towards efficient and scale-robust ultra-high-definition image demoire´ing. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVIII, pages 646–662. Springer, 2022.
[19]
Shanxin Yuan, Radu Timofte, Gregory Slabaugh, Alesˇ Leonardis, Bolun Zheng, Xin Ye, Xiang Tian, Yaowu Chen, Xi Cheng, Zhenyong Fu, Aim 2019 challenge on image demoireing: Methods and results. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pages 3534–3545. IEEE, 2019.
[20]
Bolun Zheng, Shanxin Yuan, Gregory Slabaugh, and Ales Leonardis. Image demoireing with learnable bandpass filters. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3636–3645, 2020.
[21]
Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou. In-domain gan inversion for real image editing. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII 16, pages 592–608. Springer, 2020.
[22]
Wenbin Zou, Mingchao Jiang, Yunchen Zhang, Liang Chen, Zhiyong Lu, and Yi Wu. Sdwnet: A straight dilated network with wavelet transformation for image deblurring. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1895–1904, 2021.
Index Terms
- Frequency Domain Based Optimization for Image Demoireing
Index terms have been assigned to the content through auto-classification.
Recommendations
Sphere decoder with box optimisation for faster‐than‐Nyquist non‐orthogonal frequency division multiplexing
In 1975, J. E. Mazo showed the potential faster‐than‐Nyquist (FTN) gain of the single‐carrier binary signal. If the inter‐symbol interference is eliminated by an optimal detector, FTN single‐carrier binary signal can transmit 24.7% more bits than the ...
Image restoration viawiener filtering in the frequency domain
In this paper, first, the performance of the Wiener filter in the frequency domain for image restoration is compared with that in the space domain on images degraded by white noise. After finding that the Wiener filter in the frequency domain is more ...
PAPR Reduction using Frequency Domain Multiplexed Pilot Sequences
Proceedings of the 2007 IEEE Wireless Communications and Networking ConferenceWe investigate the feasibility of applying the peak-to-average power ratio (PAPR) reduction method using pilot sequences, originally proposed for orthogonal frequency division multiplexing (OFDM) signals, for single-carrier (SC) signals with frequency ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
September 2023
133 pages
ISBN:9798400708312
DOI:10.1145/3625343
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 November 2023
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
AIMLR 2023
AIMLR 2023: 2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics
September 15 - 17, 2023
Bangkok, Thailand
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 71Total Downloads
- Downloads (Last 12 months)60
- Downloads (Last 6 weeks)4
Reflects downloads up to 18 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format