DIME-Net: A Dual-Illumination Adaptive Enhancement Network Based on Retinex and Mixture-of-Experts

• URL: http://arxiv.org/abs/2508.13921v1
• Date: Tue, 19 Aug 2025 15:17:47 GMT
• Title: DIME-Net: A Dual-Illumination Adaptive Enhancement Network Based on Retinex and Mixture-of-Experts
• Authors: Ziang Wang, Xiaoqin Wang, Dingyi Wang, Qiang Li, Shushan Qiao,
• Abstract summary: We propose a dual-illumination enhancement framework called DIME-Net.<n>By integrating Retinex theory, this module effectively performs enhancement tailored to both low-light and backlit images.<n>We show that DIME-Net achieves competitive performance on both synthetic and real-world low-light and backlit datasets without any retraining.
• Score: 7.6894262288762665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image degradation caused by complex lighting conditions such as low-light and backlit scenarios is commonly encountered in real-world environments, significantly affecting image quality and downstream vision tasks. Most existing methods focus on a single type of illumination degradation and lack the ability to handle diverse lighting conditions in a unified manner. To address this issue, we propose a dual-illumination enhancement framework called DIME-Net. The core of our method is a Mixture-of-Experts illumination estimator module, where a sparse gating mechanism adaptively selects suitable S-curve expert networks based on the illumination characteristics of the input image. By integrating Retinex theory, this module effectively performs enhancement tailored to both low-light and backlit images. To further correct illumination-induced artifacts and color distortions, we design a damage restoration module equipped with Illumination-Aware Cross Attention and Sequential-State Global Attention mechanisms. In addition, we construct a hybrid illumination dataset, MixBL, by integrating existing datasets, allowing our model to achieve robust illumination adaptability through a single training process. Experimental results show that DIME-Net achieves competitive performance on both synthetic and real-world low-light and backlit datasets without any retraining. These results demonstrate its generalization ability and potential for practical multimedia applications under diverse and complex illumination conditions.

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