Progressive Joint Low-light Enhancement and Noise Removal for Raw Images

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 definition of the captured images, we design two branches: a coefficient estimation branch as well as a joint enhancement and denoising branch. The coefficient estimation branch works in a low-resolution space and predicts the coefficients for enhancement via bilateral learning, whereas the joint enhancement and denoising branch works in a full-resolution space and progressively performs joint enhancement and denoising. In contrast to existing methods, our framework does not need to recollect massive data when being adapted to another camera model, which significantly reduces the efforts required to fine-tune our approach for practical usage. Through extensive experiments, we demonstrate its great potential in real-world low-light imaging applications when compared with current state-of-the-art methods.