An unsupervised fine-tuning strategy for low-light image enhancement

The primary goal of low-light image enhancement (LLIE) algorithms is to improve the visibility of images taken in poor lighting conditions, thereby enhancing the performance of subsequent tasks. However, relying on a single LLIE algorithm often fails to consistently address aspects like color restoration, noise reduction, brightness adjustment, and detail preservation due to varying implementation strategies. To overcome this limitation, we propose an unsupervised fine-tuning strategy that integrates multiple LLIE methods for better and more comprehensive results. Our approach consists of two phases: in the preprocessing phase, we select two complementary LLIE algorithms, Retinexformer and RQ-LLIE, to process the input low-light image independently. The enhanced outputs are designated as preprocessed images. In the unsupervised fusion fine-tuning phase, a lightweight UNet network extracts features from these preprocessed images to produce a fused image, constrained by a hybrid loss function. This function ensures consistency in image content and adjusts quality based on color, spatial consistency, and exposure. We also employ an image quality screening mechanism to select the optimal final enhanced image from the iterative outputs. Extensive experiments on benchmark datasets confirm that our algorithm outperforms existing individual LLIE methods in both qualitative and quantitative evaluations. Moreover, our approach is highly extensible, allowing for the integration of future LLIE algorithms to achieve even better results.