Transformer architecture with illumination aware mechanisms for low-light image enhancement via Retinex decomposition

Abstract

Enhancing low-light images is a complex task that involves not only restoring brightness but also preserving color fidelity and reducing noise interference. In this paper, we propose a novel Retinex-based Transformer Model with Illumination Aware Mechanisms (TIMRetinex-Net), which achieves physically interpretable modeling through a decomposition network guided by Retinex theory. To adapt to light variations in different regions, we randomly apply gamma transformations to several subregions of the illumination component and use a Color Estimation Module to capture the color global distribution of the natural scene in the reflection component. By modeling the color global distribution and repairing the degraded regions collaboratively, we alleviate the issue of being highly sensitive to data usage during training and improve the model’s ability to handle unknown scenes. The Illumination and Reflection Adjustment Transformer Network (IRAT-Net) produces enhanced images, achieving a balanced enhancement of detail and color. In addition, IRAT-Net incorporates an attention mechanism into the feature extraction layer and introduces the Illumination-Guided Information Aggregation Module to adaptively estimate lighting conditions. In the field of image processing, our method based on artificial intelligence was evaluated on five datasets and compared with twelve state-of-the-art methods. The results demonstrated strong alignment with the ground truth, with our method achieving superior performance in both subjective and objective assessments.