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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
<!-- Replace the content tag with appropriate information -->
<meta name="description" content="Multi-view Self-supervised Disentanglement for General Image Denoising">
<meta property="og:title" content="Multi-view Self-supervised Disentanglement for General Image Denoising"/>
<meta property="og:description" content="Multi-view Self-supervised Disentanglement for General Image Denoising"/>
<meta property="og:url" content="https://chqwer2.github.io/MeD/"/>
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<title>Multi-view Self-supervised Disentanglement for General Image Denoising</title>
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<style>
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margin-top: 50px;
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border: none;
outline: none;
cursor: pointer;
font-size: 2rem;
color: white;
background-image: url('./static/images/CBSD_noisy.jpg');
background-size: cover;
}
#more_cbsd:hover {
border: none;
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background-image: url('./static/images/CBSD_denoised.jpg');
background-size: cover;
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box-sizing: border-box;
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</style>
</head>
<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">Multi-view Self-supervised Disentanglement for General
Image Denoising</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://chqwer2.github.io/"
target="_blank">Hao Chen</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://chenyuanqu.com/"
target="_blank">Chenyuan Qu</a><sup>1</sup>,</span>
Yu Zhang<sup>2</sup>,</span>
<span class="author-block">
<a href="https://www.crcv.ucf.edu/chenchen/"
target="_blank">Chen Chen</a><sup>3</sup>,</span>
<a href="https://jianbojiao.com/" target="_blank">Jianbo Jiao</a><sup>1</sup>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">ICCV 2023</span>
<div style="display: flex; justify-content: space-around">
<span class="eql-cntrb" style="font-size: 1.25rem!important"><small><br><sup>1</sup>University of Birmingham</small></span>
<span class="eql-cntrb" style="font-size: 1.25rem!important"><small><br><sup>2</sup>Shanghai Jiao Tong University</small></span>
<span class="eql-cntrb" style="font-size: 1.25rem!important"><small><br><sup>3</sup>University of Central Florida</small></span>
</div>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- Arxiv PDF link -->
<span class="link-block">
<a href="static/pdfs/ICCV2023_MeD_Final_Version.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a href="https://arxiv.org/abs/2309.05049" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Supplementary PDF link -->
<span class="link-block">
<a href="static/pdfs/ICCV2023_MeD_Supplymentary_Final_Version.pdf"
target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Supplementary</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a href="https://github.com/chqwer2/Multi-view-Self-supervised-Disentanglement-Denoising"
target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<span class="link-block"
style="cursor: not-allowed; pointer-events: none; opacity: 0.5">
<a href=""
target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<img src="static/images/colab-logo.svg" alt="colab-logo">
</span>
<span>Colab (Coming Soon)</span>
</a>
</span>
<span class="link-block"
style="cursor: not-allowed; pointer-events: none; opacity: 0.5"
>
<a href=""
target="_blank"
class="external-link button is-normal is-rounded is-dark"
>
<span>🤗
</span>
<span> Huggingface (Coming Soon)</span>
</a>
</span>
<!-- <!– ArXiv abstract Link –>-->
<!-- <span class="link-block">-->
<!-- <a href="https://arxiv.org/abs/<ARXIV PAPER ID>" target="_blank"-->
<!-- class="external-link button is-normal is-rounded is-dark">-->
<!-- <span class="icon">-->
<!-- <i class="ai ai-arxiv"></i>-->
<!-- </span>-->
<!-- <span>arXiv</span>-->
<!-- </a>-->
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</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Teaser video-->
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body" style="padding-left: 0rem; padding-right: 0rem">
<img src="static/images/arc.png" style="width: 100%; height: auto"/>
<!-- <h2 class="subtitle has-text-centered">-->
<!-- Method Overview-->
<!-- </h2>-->
</div>
</div>
</section>
<!-- End teaser video -->
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
<i>
With its significant performance improvements, deep learning paradigm has become a standard
tool
for modern image denoisers. While promising performance has been shown on seen noise
distributions, existing approaches often suffer from generalization to unseen noise types or
general and real noise. It is understandable as the model is designed to learn paired
mapping
(E.g., from a noisy image to its clean version).
In this paper, we instead propose to learn to disentangle the noisy image, under the
intuitive
assumption that different corrupted versions of the same clean image share a common latent
space. A self-supervised learning framework is proposed to achieve the goal, without looking
at
the latent clean image. By taking two different corrupted versions of the same image as
input,
the proposed <b>M</b>ulti-view S<b>e</b>lf-supervised <b>D</b>isentanglement (MeD)
approach learn to disentangle the latent clean features from the corruptions and recover the
clean image consequently.
Extensive experimental analysis on both synthetic noise and real noise shows the superiority
of
the proposed method over prior self-supervised approaches, especially on unseen novel noise
types. On real noise, the proposed method even outperforms its supervised counterparts by
over
3.
</i>
</p>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Visualisation Result</h2>
<center>
<button type="submit" id="more_cbsd">
Put on your mouse!
</button>
<h2 class="subtitle has-text-centered" style=" padding-bottom: 3vw">
Visualisation result of CBSD68 dataset with local variance Gaussian noise +
Poisson noise
</h2>
</center>
<div style="display: flex; justify-content: space-between">
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage1" src="static/images/PolyU/data30.JPG" width="368" height="245" alt="data30">
<div id="myresult1" class="img-zoom-result"></div>
</div>
</div>
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage2" src="static/images/PolyU/data30_denoised.jpg" width="368" height="245"
alt="data30_denoising">
<div id="myresult2" class="img-zoom-result"></div>
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</div>
<script>
imageZoom("myimage1", "myresult1", "myimage2", "myresult2");
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</div>
<h2 class="subtitle has-text-centered" style=" padding-bottom: 3vw">
Visualisation result of PolyU dataset (Left: Noisy image; Right: Denoised image)
</h2>
<div style="display: flex; justify-content: space-between">
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage3" src="static/images/PolyU/data18.JPG" width="368" height="245" alt="data18">
<div id="myresult3" class="img-zoom-result"></div>
</div>
</div>
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage4" src="static/images/PolyU/data18_denoised.jpg" width="368" height="245"
alt="data18_denoising">
<div id="myresult4" class="img-zoom-result"></div>
</div>
</div>
<script>
imageZoom("myimage3", "myresult3", "myimage4", "myresult4");
</script>
</div>
<h2 class="subtitle has-text-centered" style=" padding-bottom: 3vw">
Visualisation result of PolyU dataset (Left: Noisy image; Right: Denoised image)
</h2>
<div style="display: flex; justify-content: space-between">
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage5" src="static/images/PolyU/data22.JPG" width="368" height="245" alt="data22">
<div id="myresult5" class="img-zoom-result"></div>
</div>
</div>
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage6" src="static/images/PolyU/data22_denoised.jpg" width="368" height="245"
alt="data22_denoising">
<div id="myresult6" class="img-zoom-result"></div>
</div>
</div>
<script>
imageZoom("myimage5", "myresult5", "myimage6", "myresult6");
</script>
</div>
<h2 class="subtitle has-text-centered" style=" padding-bottom: 3vw">
Visualisation result of PolyU dataset (Left: Noisy image; Right: Denoised image)
</h2>
<div style="display: flex; justify-content: space-between">
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage7" src="static/images/PolyU/data27.JPG" width="368" height="245" alt="data27">
<div id="myresult7" class="img-zoom-result"></div>
</div>
</div>
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage8" src="static/images/PolyU/data27_denoised.jpg" width="368" height="245"
alt="data27_denoising">
<div id="myresult8" class="img-zoom-result"></div>
</div>
</div>
<script>
imageZoom("myimage7", "myresult7", "myimage8", "myresult8");
</script>
</div>
<h2 class="subtitle has-text-centered" style=" padding-bottom: 3vw">
Visualisation result of PolyU dataset (Left: Noisy image; Right: Denoised image)
</h2>
<div style="display: flex; justify-content: space-between">
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage9" src="static/images/PolyU/data21.JPG" width="368" height="245" alt="data21">
<div id="myresult9" class="img-zoom-result"></div>
</div>
</div>
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage10" src="static/images/PolyU/data21_denoised.jpg" width="368" height="245"
alt="data21_denoising">
<div id="myresult10" class="img-zoom-result"></div>
</div>
</div>
<script>
imageZoom("myimage9", "myresult9", "myimage10", "myresult10");
</script>
</div>
<h2 class="subtitle has-text-centered" style=" padding-bottom: 3vw">
Visualisation result of PolyU dataset (Left: Noisy image; Right: Denoised image)
</h2>
<div style="display: flex; justify-content: space-between">
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage11" src="static/images/PolyU/data23.JPG" width="368" height="245" alt="data23">
<div id="myresult11" class="img-zoom-result"></div>
</div>
</div>
<div class="img-zoom-container">
<div style="display: flex">
<img id="myimage12" src="static/images/PolyU/data23_denoised.jpg" width="368" height="245"
alt="data23_denoising">
<div id="myresult12" class="img-zoom-result"></div>
</div>
</div>
<script>
imageZoom("myimage11", "myresult11", "myimage12", "myresult12");
</script>
</div>
<h2 class="subtitle has-text-centered" style=" padding-bottom: 3vw">
Visualisation result of PolyU dataset (Left: Noisy image; Right: Denoised image)
</h2>
</div>
</div>
</div>
</section>
<!-- Image carousel -->
<section class="hero is-small is-light">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">More Result Visualisation</h2>
<div id="results-carousel" class="carousel results-carousel">
<center>
<div class="item" style="width: 960px;">
<!-- Your image here -->
<img src="static/images/noiselocal_val_gaussian@25_15.jpg"
alt="Kodak"/>
<h2 class="subtitle has-text-centered">
Visual comparison of image denoising methods on Kodak images with Gaussian (σ = 25) + local
variance Gaussian noise
</h2>
</div>
</center>
<center>
<div class="item" style="width: 960px;">
<!-- Your image here -->
<img src="static/images/real_50.jpg"
alt="real_50"
style="max-height: 50%"
/>
<h2 class="subtitle has-text-centered">
Visual comparison of image denoising methods on real noisy image dataset SIDD example images
with real noise.
</h2>
</div>
</center>
<center>
<div class="item" style="width: 960px;">
<!-- Your image here -->
<img src="static/images/real_78.jpg"
alt="real_78"/>
<h2 class="subtitle has-text-centered">
Visual comparison of image denoising methods on real noisy image dataset SIDD example images
with real noise.
</h2>
</div>
</center>
<center>
<div class="item" style="width: 960px;">
<!-- Your image here -->
<img src="static/images/real_97.jpg"
alt="real_97"/>
<h2 class="subtitle has-text-centered">
Visual comparison of image denoising methods on real noisy image dataset SIDD example images
with real noise.
</h2>
</div>
</center>
<!-- <div class="item" style="width: 960px;">-->
<!-- <!– Your image here –>-->
<!-- <img src="static/images/Denoising%20Results%20on%20unseen%20noise%20types.jpg"-->
<!-- alt="Unseen visualisation" class="center-image blend-img-background"/>-->
<!-- <h2 class="subtitle has-text-centered">-->
<!-- Qualitative denoising results on unseen noise types. All the methods are trained with Gaussian σ-->
<!-- = 25. The quantitative PSNR/SSIM results are provided underneath the respective images. Best-->
<!-- viewed in colour (zoom-in for a better comparison).-->
<!-- </h2>-->
<!-- </div>-->
<!-- <div class="item" style="width: 460px;">-->
<!-- <!– Your image here –>-->
<!-- <img src="static/images/Denoising%20Results%20on%20SIDD.jpg" alt="SIDD visualisation"/>-->
<!-- <h2 class="subtitle has-text-centered">-->
<!-- Qualitative denoising results on unseen noise types. All the methods are trained with Gaussian σ-->
<!-- = 25. The quantitative PSNR/SSIM results are provided underneath the respective images. Best-->
<!-- viewed in colour (zoom-in for a better comparison).-->
<!-- </h2>-->
<!-- </div>-->
</div>
</div>
</div>
</section>
<!-- End image carousel -->
<!-- Paper poster -->
<section class="hero is-small is-light">
<div class="hero-body">
<div class="container">
<h2 class="title">Poster</h2>
<!-- <iframe src="static/pdfs/ICCV23_MeD_Poster%20(1)_20230930132936.pdf" width="100%" height="550">-->
<!-- </iframe>-->
<a href="static/pdfs/ICCV23_MeD_Poster%20(1)_20230930132936.pdf" target="_blank">
<img src="static/pdfs/ICCV23_MeD_Poster%20(1)_20230930132936.jpg" width="100%" height="auto"/>
</a>
</div>
</div>
</section>
<!--End paper poster-->
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@InProceedings{MeD_ICCV23,
author = {Chen, Hao and Qu, Chenyuan and Zhang, Yu and Chen, Chen and Jiao, Jianbo},
title = {Multi-view Self-supervised Disentanglement for General Image Denoising},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
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