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<!DOCTYPE html>
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<title>Multiscale Coarse-to-Fine Guided Screenshot Demoiréing</title>
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<h1 class="title is-1 publication-title" style="color: #ffffff;">Multiscale Coarse-to-Fine Guided Screenshot Demoiréing</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="mailto:[email protected]" target="_blank">Duong Hai Nguyen</a><sup>1</sup>,</span>
<span class="author-block">
<a href="mailto:[email protected]" target="_blank">Se-Ho Lee</a><sup>2</sup>, and </span>
<span class="author-block">
<a href="mailto:[email protected]" target="_blank">Chul Lee</a><sup>1</sup>
</span>
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<span class="author-block">
<sup>1</sup>Department of Multimedia Engineering, Dongguk University, South Korea<br>
<sup>2</sup>Department of Information and Engineering, Jeonbuk National University, South Korea<br>
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IEEE Signal Processing Letters 2023
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<h2 class="subtitle has-text-centered" style="color: #ffffff;">
Qualitative comparisons of demoireing results on the UHDM dataset.<br>
Given a moiré image, from left to right, top to bottom, the magnified views show the results of
<a href="https://ieeexplore.ieee.org/document/8356681" target="_blank">DMCNN</a>,
<a href="https://arxiv.org/abs/1909.11947" target="_blank">MDDM</a>,
<a href="https://arxiv.org/abs/2007.07173" target="_blank">WDNet</a>,
<a href="https://openaccess.thecvf.com/content_ICCV_2019/html/He_Mop_Moire_Patterns_Using_MopNet_ICCV_2019_paper.html" target="_blank">MopNet</a>,
<a href="https://ieeexplore.ieee.org/document/9547736" target="_blank">MBCNN</a>,
<a href="https://link.springer.com/chapter/10.1007/978-3-030-58542-6_43" target="_blank">FHDe²Net</a>,
<a href="https://arxiv.org/abs/2207.09935" target="_blank">ESDNet</a>,
ours, and ground truth.
</h2>
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<h2 class="title is-3" style="color: #ffffff;">Abstract</h2>
<div class="content has-text-justified">
<p>
In this letter, we propose a multiscale coarse-to-fine guided screenshot demoiréing algorithm. We first extract the multiscale feature of the input image. Then, we develop the multiscale guided restoration block (MGRB), which removes moiré patterns with the guidance of multiscale information by exploiting the correlation between moiré frequencies. To this end, we design two blocks for feature modulation and moiré pattern removal. In addition, to further improve the performance, we develop an adaptive reconstruction loss to direct the network to focus on regions that are difficult to restore. Experimental results on multiple datasets demonstrate that the proposed algorithm provides comparable or even better demoiréing performance than state-of-the-art algorithms.
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<h2 class="title is-3" style="color: #ffffff;">Network Architecture & Quantitative Comparison</h2>
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<pre style="background-color: #424242; color: #d1d1d1;"><code>
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@article{2023_nguyen_gad,
author = {Duong Hai, Nguyen and Se-Ho, Lee and Chul, Lee},
title = {Multiscale Coarse-to-Fine Guided Screenshot Demoiréing},
journal = {IEEE Signal Processing Letters},
volume = {30},
number = {},
pages = {898-902},
month = jul,
year = {2023},
doi = {10.1109/LSP.2023.3296039}
}
</code></pre>
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</section>
<!--End BibTex citation -->
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