Mop Moire Patterns Using MopNet

Bin He, Ce Wang, Boxin Shi, Ling-Yu Duan; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 2424-2432

Abstract


Moire pattern is a common image quality degradation caused by frequency aliasing between monitors and cameras when taking screen-shot photos. The complex frequency distribution, imbalanced magnitude in colour channels, and diverse appearance attributes of moire pattern make its removal a challenging problem. In this paper, we propose a Moire pattern Removal Neural Network (MopNet) to solve this problem. All core components of MopNet are specially designed for unique properties of moire patterns, including the multi-scale feature aggregation addressing complex frequency, the channel-wise target edge predictor to exploit imbalanced magnitude among colour channels, and the attribute-aware classifier to characterize the diverse appearance for better modelling Moire patterns. Quantitative and qualitative experimental comparison validate the state-of-the-art performance of MopNet.

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[bibtex]
@InProceedings{He_2019_ICCV,
author = {He, Bin and Wang, Ce and Shi, Boxin and Duan, Ling-Yu},
title = {Mop Moire Patterns Using MopNet},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}