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Computer Science > Computer Vision and Pattern Recognition

arXiv:2308.06725 (cs)
[Submitted on 13 Aug 2023 (v1), last revised 28 Aug 2023 (this version, v2)]

Title:CLE Diffusion: Controllable Light Enhancement Diffusion Model

Authors:Yuyang Yin, Dejia Xu, Chuangchuang Tan, Ping Liu, Yao Zhao, Yunchao Wei
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Abstract: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. Project page: this https URL
Comments: Accepted In Proceedings of the 31st ACM International Conference on Multimedia (MM' 23)
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
Cite as: arXiv:2308.06725 [cs.CV]
  (or arXiv:2308.06725v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2308.06725
Related DOI: https://doi.org/10.1145/3581783.3612145

Submission history

From: Yuyang Yin [view email]
[v1] Sun, 13 Aug 2023 09:05:56 UTC (5,288 KB)
[v2] Mon, 28 Aug 2023 04:27:35 UTC (5,288 KB)
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