NamPnP: Noise-Aware mechanism within Plug-and-Play framework for image enhancement

Abstract

Low-light Image Enhancement (LIE) strives to improve contrast and restore details for images captured in dark conditions. Most of the previous LIE algorithms were developed based on the Retinex theory, which decomposes the observed image into illumination and reflectance components for pertinent processing. However, most of such methods that address the noise issue of the reflectance component regard the noise as Gaussian noise, which limits the applicability to diverse noise conditions. In this paper, we employ an appropriate noise degradation model in the designed Noise-Aware network to achieve the suppression of various noises in real-world scenarios. Specifically, the designed network leverages the powerful modeling capabilities of the Transformer to better integrate with the proposed degradation model, effectively eliminating noise with unknown distributions in real-world scenarios. Subsequently, it is plugged into the Retinex-based framework to achieve better enhancement performance. Additionally, the proposed method incorporates an edge-guided adaptive weight matrix and an iterative process to regularize the illumination component, resulting in more natural decomposition and further promoting the harmonious integration of illumination and reflectance components. Extensive evaluations on public datasets reveal that the proposed method outperforms existing techniques both qualitatively and quantitatively, demonstrating superior performance in noise removal under dark conditions while preserving finer texture and structural details.