Zero-Shot Low Light Image Enhancement with Diffusion Prior

• URL: http://arxiv.org/abs/2412.13401v1
• Date: Wed, 18 Dec 2024 00:31:18 GMT
• Title: Zero-Shot Low Light Image Enhancement with Diffusion Prior
• Authors: Joshua Cho, Sara Aghajanzadeh, Zhen Zhu, D. A. Forsyth,
• Abstract summary: We introduce a novel zero-shot method for controlling and refining the generative behavior of diffusion models for dark-to-light image conversion tasks.<n>Our method demonstrates superior performance over existing state-of-the-art methods in the task of low-light image enhancement.
• Score: 2.102429358229889
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Balancing aesthetic quality with fidelity when enhancing images from challenging, degraded sources is a core objective in computational photography. In this paper, we address low light image enhancement (LLIE), a task in which dark images often contain limited visible information. Diffusion models, known for their powerful image enhancement capacities, are a natural choice for this problem. However, their deep generative priors can also lead to hallucinations, introducing non-existent elements or substantially altering the visual semantics of the original scene. In this work, we introduce a novel zero-shot method for controlling and refining the generative behavior of diffusion models for dark-to-light image conversion tasks. Our method demonstrates superior performance over existing state-of-the-art methods in the task of low-light image enhancement, as evidenced by both quantitative metrics and qualitative analysis.

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