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<title>ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration</title>
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<h1 class="title is-1 publication-title">🌆 <b>ProRes:</b> <br>Exploring Degradation-aware Visual <strong>Pro</strong>mpt for Universal Image <strong>Res</strong>toration</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://leonmakise.github.io/" target="_blank">Jiaqi Ma</a><sup>1,✢</sup>,</span>
<span class="author-block">
<a href="https://github.com/wondervictor" target="_blank">Tianheng Cheng</a><sup>2,✢</sup>,</span>
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<a href="https://scholar.google.com/citations?user=z-25fk0AAAAJ" target="_blank">Guoli Wang</a><sup>3</sup>,
</span>
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<a href="https://scholar.google.com/citations?user=qNCTLV0AAAAJ" target="_blank">Xinggang Wang</a><sup>2</sup>,
</span>
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<a href="https://scholar.google.com/citations?user=pCY-bikAAAAJ" target="_blank">Qian Zhang</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=BLKHwNwAAAAJ" target="_blank">Lefei Zhang</a><sup>1,📧</sup>
</span>
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<span class="author-block">Wuhan University<sup>1</sup><br>Huazhong University of Science & Technology<sup>2</sup><br>Horizon Robotics<sup>3</sup><br><b>Under Peer Review</b></span>
<span class="eql-cntrb"><small><br><sup>✢</sup>: Equal Contribution</small>, <small><sup>📧</sup>: Corresponding Author</small></span>
</div>
<h4 class="title is-4">TL;NR: The First Visual Prompt Based All-in-one Image Restoration Framework</h4>
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</section>
<!-- Teaser video-->
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<h2 class="title is-3">All-in-one Image Restoration</h2>
<div class="hero-body">
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<h2 class="subtitle has-text-centered">
Conceptual comparison with previous approaches.
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<h2 class="title is-3">Abstract</h2>
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<p>
Image restoration aims to reconstruct degraded images, e.g., denoising or deblurring. Existing works focus on designing task-specific methods and there are inadequate attempts at universal methods. However, simply unifying multiple tasks into one universal architecture suffers from uncontrollable and undesired predictions. To address those issues, we explore prompt learning in universal architectures for image restoration tasks. In this paper, we present Degradation-aware Visual Prompts, which encode various types of image degradation, e.g., noise and blur, into unified visual prompts. These degradation-aware prompts provide control over image processing and allow weighted combinations for customized image restoration. We then leverage degradation-aware visual prompts to establish a controllable and universal model for image restoration, called ProRes, which is applicable to an extensive range of image restoration tasks. ProRes leverages the vanilla Vision Transformer (ViT) without any task-specific designs. Furthermore, the pre-trained ProRes can easily adapt to new tasks through efficient prompt tuning with only a few images. Without bells and whistles, ProRes achieves competitive performance compared to task-specific methods and experiments can demonstrate its ability for controllable restoration and adaptation for new tasks.
</p>
</div>
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<h2 class="title is-3">Overall Pipeline of ProRes</h2>
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<h2 class="subtitle has-text-lefted">
<b>(a) Training ProRes:</b> we add the target visual prompt to the input image and flatten the prompted image into patches. We leverage a vision transformer, i.e., ViT-Large, as the image encoder and adopt a simple pixel decoder to generate the restored image. Then we adopt pixel loss to optimize ProRes. <br>
<b>(b) Prompt Tuning:</b> we freeze the weights of ProRes and randomly initialize the learnable prompts for new tasks or new datasets.
</h2>
</div>
</div>
</div>
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<h2 class="title is-3">Control Ability</h2>
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<h3 class="title is-4">Independent Control</h3>
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<h2 class="subtitle has-text-centered">
Visualization results processed from images of different corruptions. Compared with the original inputs, the outputs are consistent with the given visual prompts.
</h2>
</div>
<div class="item">
<h3 class="title is-4">Sensitive to Irrelevant Task-specific Prompts</h3>
<!-- Your image here -->
<img src="figures/S2_irrelevant.jpg" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Visualization results processed by different prompts. Compared with the original inputs, the outputs remain unchanged with irrelevant visual prompts.
</h2>
</div>
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<h3 class="title is-4">Tackle Complicated Corruptions</h3>
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<h2 class="subtitle has-text-centered">
Visualization results processed by ProRes from images of mixed types of degradation, i.e., low-light and rainy. ProRes adopts two visual prompts for low-light enhancement (E) and deraining (D) and combines the two visual prompts by linear weighted sum, i.e., αD + (1 − α)E, to control the restoration process.
</h2>
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<h2 class="title is-3">Adaptation on New Datasets & Task</h2>
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<h3 class="title is-4">Low-light Enhancement Results</h3>
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<h2 class="subtitle has-text-centered">
Visualization results of ProRes on the FiveK dataset. We adopt two settings, i.e., direct inference and prompt tuning, to evaluate ProRes on the FiveK dataset (a new dataset for low-light enhancement).
</h2>
</div>
<div class="item">
<h3 class="title is-4">Dehazing Results</h3>
<!-- Your image here -->
<img src="figures/tuning_reside.jpg" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Visualization results of ProRes on the RESIDE-6K dataset via prompt tuning for image dehazing (a new task).
</h2>
</div>
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</div>
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<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{ma2023prores,
title={Prores: Exploring degradation-aware visual prompt for universal image restoration},
author={Ma, Jiaqi and Cheng, Tianheng and Wang, Guoli and Zhang, Qian and Wang, Xinggang and Zhang, Lefei},
journal={arXiv preprint arXiv:2306.13653},
year={2023}
}</code></pre>
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</section>
<!--End BibTex citation -->
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