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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
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<meta name="description" content="Project Page About CLE Diffusion: Controllable Light Enhancement Diffusion Model">
<meta property="og:title" content="CLE Diffusion: Controllable Light Enhancement Diffusion Model"/>
<meta property="og:description" content="Project Page About CLE Diffusion: Controllable Light Enhancement Diffusion Model"/>
<meta property="og:url" content=" https://yuyangyin.github.io/CLEDiffusion/"/>
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<meta name="keywords" content="image processing, low light image enhancement, diffusion model">
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<title>CLE Diffusion: Controllable Light Enhancement Diffusion Model(ACM MM 2023)</title>
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<h1 class="title is-1 publication-title">CLE Diffusion: Controllable Light Enhancement Diffusion Model(MM 2023)</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://yuyangyin.github.io/" target="_blank">Yuyang Yin</a><sup>1</sup><sup>,2</sup>,</span>
<span class="author-block">
<a href="https://ir1d.github.io/" target="_blank">Dejia Xu</a><sup>3</sup>,</span>
<span class="author-block">
Chuangchuang Tan<sup>1</sup><sup>,2</sup>,</span>
<!-- <a href="https://ir1d.github.io/" target="_blank">Chuangchuang Tan</a><sup>3</sup>,</span>-->
<span class="author-block">
<a href="https://sites.google.com/site/pingliu264/" target="_blank">Ping Liu</a><sup>4</sup>,</span>
<span class="author-block">
<a href="http://faculty.bjtu.edu.cn/5900/" target="_blank">Yao Zhao</a><sup>1</sup><sup>,2</sup>,</span>
<span class="author-block">
<a href="https://weiyc.github.io/index.html" target="_blank">Yunchao Wei</a><sup>1</sup><sup>,2</sup>,</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><br><sup>1</sup>Institute of Information Science, Beijing Jiaotong University<br></span>
<span class="author-block"><br><sup>2</sup>Beijing Key Laboratory of Advanced Information Science and Network Technology<br></span>
<span class="author-block"><br><sup>3</sup>VITA Group, University of Texas at Austin<br></span>
<span class="author-block"><br><sup>4</sup>Center for Frontier AI Research, IHPC, A*STAR<br></span>
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<span>Paper</span>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
Low light enhancement has gained increasing importance with the rapid development of visual creation and editing.
However, most existing enhancement algorithms are designed to homogeneously increase the brightness of images to a pre-defined extent, limiting the user experience.
To address this issue, we propose Controllable Light Enhancement Diffusion Model, dubbed CLE Diffusion,
a novel diffusion framework to provide users with rich controllability. Built with a conditional diffusion model,
we introduce an illumination embedding to let users control their desired brightness level.
Additionally, we incorporate the Segment-Anything Model (SAM) to enable user-friendly region controllability,
where users can click on objects to specify the regions they wish to enhance.
Extensive experiments demonstrate that CLE Diffusion achieves competitive performance regarding quantitative metrics, qualitative results, and versatile controllability.
</div>
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</section>
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<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Method</h2>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<div class="item" >
<img src="static/images/framework.png" alt="MY ALT TEXT">
<div class="content has-text-justified">
<div class="formula">
<p>
During training, we randomly sample a pair of low-light image <span>`x`</span> and normal-light image `y`. We then construct `y_t`, color map `C(x)`, and snr map `S(x)` as additional inputs to the diffusion model.
We extract brightness level `\lambda` of normal-light image by cacluating the average pixel value. Then `\lambda` is injected into the Brightness Control Modules to enable seamless and consistent brightness control.
Alongside <span>$L_\text{simple}$</span>, we introduce auxiliary losses on the denoised estimate `\hat{y_0}` to provide better supervision for the model.
</p>
<p>
To achieve regional controllability, We incorporate a binary mask `M` into our diffusion model by
concatenating the mask with the original inputs. To accommodate
this requirement, we created synthetic training data by randomly
sampling free-form masks with feathered boundaries. The
target images are generated by alpha blending the low-light and
normal-light images from existing low-light datasets.
</p>
<p>
</p>
</div>
<div style="display: flex; justify-content: center;">
<img src="static/images/Algorithm.png " alt="MY ALT TEXT" style="width: auto; height: 250px;"></div>
<div class="content has-text-justified">
<div class="formula">
<p>
The sampling process is implemented with DDIM sampler. We use classifier free guide method to estimate two noise from a
conditional model and a unconditional model. Armed with SAM, CLE Diffusion achieve light enhancement with specified regions and designated levels of brightness.
</p>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small is-light">
<div class="hero-body">
<div class="container">
<h2 class="title is-3"> Qualitative Results</h2>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<div class="item" >
<img src="static/images/lolmittable.png" alt="MY ALT TEXT">
</div>
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<!--<!– Table 2: Comparisons on the MIT-Adobe FiveK dataset.–>-->
<!--<!– </h2>–>-->
<!-- </div>-->
<!-- <h2>-->
<!-- Existing quantitative metrics typically assume the existence of an ideal brightness level, making it difficult to compare images with-->
<!-- different brightness levels fairly. We introduce a novel metric named <strong>LI-LPIPS</strong>, which is more stable when brightness changes and better assesses image quality.-->
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</div>
</div>
</div>
</div>
</div>
</section>
<!-- Image carousel -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Visual Results</h2>
<!-- <div id="results-carousel" class="carousel results-carousel">-->
<div class="item">
<!-- Your image here -->
<img src="static/images/RegionControl1.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Figure 1: CLE Diffusion enables users to select regions of interest(ROI) with a simple click and adjust the degree of brightness
enhancement as desired, while MAXIM [1] is limited to homogeneously enhancing images to a pre-defined level of brightness.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/RegionControl2.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Figure 2: More cases about region controllable light enhancement. Equipped with the Segment-Anything Model (SAM), users
can designate regions of interest (ROI) using simple inputs like points or boxes. Our model facilitates controllable light
enhancement within these regions, producing results that blend naturally and seamlessly with the surrounding environment.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/GlobalControl1.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Figure 3: Visual results of global brightness control on LOL dataset. By adjusting the brightness levels during inference, we can sample images
with varying degrees of brightness while maintaining high image quality
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/GlobalControl2.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Figure 4: Global Controllable Light Enhancement on MIT-Adobe FiveK dataset. Our method enables users to select various
brightness levels, even significantly brighter than the ground truth.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/LOL.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Figure 5: Results on LOL [2] test dataset. Our result exhibits fewer artifacts and is more consistent with the ground truth image.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/mit.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Figure 6: Results on MIT-Adobe FiveK [3] test dataset. Our result exhibits less color distortion and contains richer details,
which are more consistent with the ground truth.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/velol.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Figure 7: Comparisons on a real-world image from VE-LOL dataset [4]. Other methods often rely on well-lit brightness
extracted from pre-existing datasets, limiting their applicability to diverse scenarios. Unlike most methods that struggle to
enhance the brightness at night sufficiently, our method incorporates a brightness control module, allowing us to sample
images with higher brightness that appear more natural in such situations.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/highlight.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Figure 8: Performance on normal light image inputs. We utilize the normal-light images from the LOL dataset as inputs to
evaluate the models’ capability in handling high-light images. HWMNet and MAXIM exhibit overexposure in certain regions,
resulting in considerably over-exposed images. LLFlow produces blurred images, while other methods result in color distortion.
Our method achieves visually pleasing results in terms of color and brightness.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/recoro.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Figure 9: Global brightness control compared with ReCoRo [5]. While ReCoRo is constrained to enhancing images with
brightness levels that fall between low-light and “well-lit” images, our model can handle a wider range of brightness levels. It
can be adjusted to sample any desired brightness, providing greater flexibility and control over different lighting conditions.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/sam.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Figure 10: Performance on Segment-Anything model. SAM generates coarse masks when dealing with images captured in
low-light conditions. After enhancing with our model, the images can be effectively segmented even in dark environments.
This demonstrates our model’s ability to restore details that are friendly to high-level machine vision models.
</h2>
</div>
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<h2 class="title">Bibtex</h2>
<pre><code>
@article{yin2023cle,
title={CLE Diffusion: Controllable Light Enhancement Diffusion Model},
author={Yin, Yuyang and Xu, Dejia and Tan, Chuangchuang and Liu, Ping and Zhao, Yao and Wei, Yunchao},
journal={arXiv preprint arXiv:2308.06725},
year={2023}
}
</code></pre>
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<h2 class="title">References</h2>
<pre><code>
[1] Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li. 2022. Maxim: Multi-axis mlp for image processing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.5769–5780.
[2] Wei Chen, Wang Wenjing, Yang Wenhan, and Liu Jiaying. 2018. Deep Retinex Decomposition for Low-Light Enhancement. In British Machine Vision Conference.British Machine Vision Association.
[3] Vladimir Bychkovsky, Sylvain Paris, Eric Chan, and Frédo Durand. 2011. Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs. In The Twenty-Fourth IEEE Conference on Computer Vision and Pattern Recognition.
[4] Jiaying Liu, Dejia Xu, Wenhan Yang, Minhao Fan, and Haofeng Huang. 2021. Benchmarking low-light image enhancement and beyond. International Journal of Computer Vision 129 (2021), 1153–1184.
[5] Dejia Xu, Hayk Poghosyan, Shant Navasardyan, Yifan Jiang, Humphrey Shi, and Zhangyang Wang. 2022. ReCoRo: Region-Controllable Robust Light Enhancement with User-Specified Imprecise Masks. In Proceedings of the 30th ACM International Conference on Multimedia. 1376–1386.
</code></pre>
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