TL;DR
This paper introduces a practical, camera-agnostic framework for low-light image enhancement that jointly improves illumination, color, and noise removal through a progressive, multi-branch approach suitable for real-world applications.
Contribution
The proposed framework enables joint low-light enhancement and denoising without camera-specific data collection, using a dual-branch design for efficient adaptation and high-quality results.
Findings
Outperforms state-of-the-art methods in real-world low-light scenarios.
Reduces data collection efforts for different camera models.
Effectively combines enhancement and denoising in a progressive manner.
Abstract
Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture, resulting in a low signal-to-noise ratio. Most of the previous works on low-light image processing focus either only on a single task such as illumination adjustment, color enhancement, or noise removal; or on a joint illumination adjustment and denoising task that heavily relies on short-long exposure image pairs collected from specific camera models, and thus these approaches are less practical and generalizable in real-world settings where camera-specific joint enhancement and restoration is required. To tackle this problem, in this paper, we propose a low-light image processing framework that performs joint illumination adjustment, color enhancement, and denoising. Considering the difficulty in model-specific data collection and the ultra-high…
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