Moiré Pattern Removal with a Generative Adversarial Network
Pages 81 - 86
Abstract
Moiré patterns can be seen in camera-captured digital screen photos due to the interference between the pixel grids of the camera sensor and the pixel grids of the digital screen. It severely degrades the quality of the photos. With the rapid development of personal devices, people are using digital camera to take photos more and more often. Among them, it's very common to see camera-captured screen photos, so the work of Moiré pattern removal is very meaningful for improving user experience. In this paper, we introduce a novel method of Moiré pattern removal based on the Generative Adversarial Network (GAN). To train our model, we built a dataset of paired Ground and Moiré images, which has 16,500 images totally. Experiments showed that, given Moiré images as the input, the trained generator of our GAN nets can produce Moiré-free images of high quality.
References
[1]
Eldho Abraham. 2018. Moiré Pattern Detection using Wavelet Decomposition and Convolutional Neural Network. In Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Bangalore, India, 1275--1279.
[2]
Goodfellow Ian J, Pouget-Abadie Jean, Mirza Mehdi, Xu Bing, Warde-Farley David, Ozair Sherjil, Courville Aaron and Bengio Yoshua. 2014. Generative Adversarial Networks. arXiv:1406.2661. Retrieved from https://arxiv.org/abs/1406.2661.
[3]
Longze Li and Aleksandar Vakanski. 2018. Generative Adversarial Networks for Generation and Classification of Physical Rehabilitation Movement Episodes. International Journal of Machine Learning and Computing (Vol. 8, No. 5), 428--436.
[4]
Sreedhar Kollem, Katta Rama Linga Reddy, and Duggirala Srinivasa Rao. 2019. A Review of Image Denoising and Segmentation Methods Based on Medical Images. International Journal of Machine Learning and Computing (Vol. 9, No. 3), 288--295.
[5]
Xiang Wu, Zhi-Guo Shi, and Lei Liu. 2014. Image Denoising with Wavelet Markov Fields of Experts. International Journal of Machine Learning and Computing (Vol. 4, No. 6), 527--532.
[6]
Yujing Sun, Yizhou Yu and Wenping Wang. 2018. Moiré Photo Restoration Using Multiresolution Convolutional Neural Networks. IEEE Transactions on Image Processing (Volume: 27, Issue: 8, Aug. 2018), 4160--4172.
[7]
Fanglei Liu, Jingyu Yang and Huanjing Yue. 2015. Moiré Pattern Removal From Texture Images via Low-rank and Sparse Matrix Decomposition. In Proceedings of the 2015 Visual Communications and Image Processing (VCIP). IEEE, Singapore, 1--4.
[8]
Seyede Mahya Hazavei and Hamid Reza Shahdoosti. 2017. A New Method for Removing the Moiré Pattern from Images. arXiv:1701.09037. Retrieved from https://arxiv.org/abs/1701.09037.
[9]
LeCun, Y., Bengio, Y. and Hinton, G. 2015. Deep learning. Nature 521, 436--444.
[10]
Abeer Alsaiari, Ridhi Rustagi, A'aeshah Alhakamy, Manu Mathew Thomas and Angus G. Forbes. 2019. Image Denoising Using a Generative Adversarial Network. In Proceedings of 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT). IEEE, Kahului, HI, USA, 126--132.
[11]
Everingham Mark, Van Gool Luc, Williams Christopher K. I., Winn John Zisserman Andrew. 2010. International Journal of Computer Vision (Volume: 88, Issue: 2, Jun. 2010), 303--338.
[12]
Harris C and Stephens M. 1988. A Combined Corner and Edge Detector. In Proceedings of the 4th Alvey Vision Conference. The plessey company plc, Manchester, UK, 147--151.
[13]
Opencv. Harris Corner Detection. Retrieved from https://docs.opencv.org/master/dc/d0d/tutorial_py_features_harris.html
[14]
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, NV, USA, 770--778.
[15]
Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv:1412.6980. Retrieved from https://arxiv.org/abs/1412.6980.
Index Terms
- Moiré Pattern Removal with a Generative Adversarial Network
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Published In
June 2020
127 pages
ISBN:9781450377812
DOI:10.1145/3406971
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In-Cooperation
- University of Macedonia
- NITech: Nagoya Institute of Technology
- Zhejiang University: Zhejiang University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 July 2020
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- Refereed limited
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ICGSP 2020
ICGSP 2020: 2020 The 4th International Conference on Graphics and Signal Processing
June 26 - 29, 2020
Nagoya, Japan
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