Efficient Diffusion as Low Light Enhancer

TL;DR

This paper introduces ReDDiT, a novel diffusion-based framework for Low-Light Image Enhancement that significantly reduces computational steps while maintaining or surpassing state-of-the-art performance.

Contribution

The paper proposes the RATR module and ReDDiT framework, which improve diffusion-based LLIE by mitigating fitting errors and inference gaps, enabling efficient enhancement with fewer steps.

Findings

ReDDiT achieves comparable results with only 2 steps.

State-of-the-art performance with 4 or 8 steps.

Outperforms existing methods on 10 benchmark datasets.

Abstract

The computational burden of the iterative sampling process remains a major challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current acceleration methods, whether training-based or training-free, often lead to significant performance degradation, highlighting the trade-off between performance and efficiency. In this paper, we identify two primary factors contributing to performance degradation: fitting errors and the inference gap. Our key insight is that fitting errors can be mitigated by linearly extrapolating the incorrect score functions, while the inference gap can be reduced by shifting the Gaussian flow to a reflectance-aware residual space. Based on the above insights, we design Reflectance-Aware Trajectory Refinement (RATR) module, a simple yet effective module to refine the teacher trajectory using the reflectance component of images. Following this, we introduce \textbf{Re}flectance-aware \textbf{D}iffusion with \textbf{Di}stilled \textbf{T}rajectory (\textbf{ReDDiT}), an efficient and flexible distillation framework tailored for LLIE. Our framework achieves comparable performance to previous diffusion-based methods with redundant steps in just 2 steps while establishing new state-of-the-art (SOTA) results with 8 or 4 steps. Comprehensive experimental evaluations on 10 benchmark datasets validate the effectiveness of our method, consistently outperforming existing SOTA methods.