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<title>Neural Color Operators for Sequential Image Retouching</title>
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<h1 class="text-center">Neural Color Operators for Sequential Image Retouching</h1>
<div class="col-xs-0"></div>
<div class="col-xs-2 text-center"> </div>
<div class="col-xs-2 text-center">
<a href="http://yili.host">Yili Wang<sup>1</sup></a>
<p>Tsinghua University</p>
</div>
<div class="col-xs-2 text-center">
<a href="https://scholar.google.com/citations?user=4BEGYMwAAAAJ&hl=zh-CN/">Xin Li</a>
<p>Baidu Inc.</p>
</div>
<div class="col-xs-2 text-center">
<a href="https://cg.cs.tsinghua.edu.cn/people/~kun">Kun Xu</a>
<p>Tsinghua University</p>
</div>
<div class="col-xs-2 text-center">
<a href="">Dongliang He</a>
<p>Baidu Inc.</p>
</div>
</div>
</div>
<div class="row">
<div class="col-xs-3 text-center"> </div>
<div class="col-xs-2 text-center">
<a href="">Qi Zhang</a>
<p>Baidu Inc.</p>
</div>
<div class="col-xs-2 text-center">
<a href="">Fu Li</a>
<p>Baidu Inc.</p>
</div>
<div class="col-xs-2 text-center">
<a href="">Errui Ding</a>
<p>Baidu Inc.</p>
</div>
</div>
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<div class="col">
<h3 class="text-center">Abstract</h3>
<p>
We propose a novel image retouching method by modeling the retouching process as performing a sequence of newly introduced trainable \emph{neural color operators}. The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar. To reflect the homomorphism property of color operators, we employ equivariant mapping and adopt an encoder-decoder structure which maps the non-linear color transformation to a much simpler transformation (i.e., translation) in a high dimensional space. The scalar strength of each neural color operator is predicted using CNN based strength predictors by analyzing global image statistics. Overall, our method is rather lightweight and offers flexible controls. Experiments and user studies on public datasets show that our method consistently achieves the best results compared with SOTA methods in both quantitative measures and visual qualities.
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<h3>Paper</h3>
<p>
<a href="https://cg.cs.tsinghua.edu.cn/people/~kun/2022NeurOp/neurop.pdf">
<img src="website/img/thumb_paper.png" alt="Paper" class="img-thumbnail" style="height: 155px;"/>
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<h3>BibTeX</h3>
<p>Please cite our work if you use code or data from this site.</p>
<pre><code class="language-latex">@inproceedings{wang2022neurop,
author = {Wang, Yili and Li, Xin and Xu, Kun and He, Dongliang and Zhang, Qi and Li, Fu and Ding, Errui},
title = {Neural Color Operators for Sequential Image Retouching},
year = {2022},
isbn = {978-3-031-19800-7},
publisher = {Springer-Cham},
url = {https://doi.org/10.1007/978-3-031-19800-7_3},
doi = {10.1007/978-3-031-19800-7_3},
booktitle = {Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIX},
numpages = {14},
}</code></pre>
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<h3>Video (Paper)</h2>
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<h3>Video (Keynote at Eurographics 2021)</h2>
<p>
<iframe width="450" height="253" src="https://www.youtube.com/embed/yLLhMkctfBY?t=2360" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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<h3 class="text-center"><a href="https://github.com/amberwangyili/neurop">Code & Data</a></h3>
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<h2 class="text-center">Results</h2>
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