Unsupervised Boosted Fusion Network for Single Low-Light Image Enhancement

Abstract

Unsupervised methods are gradually becoming a research hotspot in low-light image enhancement due to their lack of paired training data and higher quality visual enhancement effects, but existing unsupervised low-light image enhancement methods generate excess noise and rely on contrast exposure stacks. To address this issue, we propose an unsupervised boosted fusion network for single low-light image enhancement. In the network, we generate four images with different illuminations and texture detail information from the input low-light image by applying inverting, fixed-value gamma correction, adaptive gamma correction, and color-preserving adaptive histogram equalization in the preprocessing stage. Then, we introduce DenseNet and design dedicated fusion weights to progressively fuse the four images generated in previous stage, which can boost the image contrast and brightness while suppressing overexposure phenomena in the bright regions. Finally, a self-supervised denoising network based on UNet++ is designed for suppressing the amplified noise while maintaining brightness, contrast, and texture details. The visual evaluation and quantitative experiments demonstrate that our proposed method outperforms the current state-of-the-art unsupervised low-light image enhancement methods. Especially, the enhancement effect in some scenes is superior to the supervised low-light image enhancement networks. The UBF-Net code is available at.https://github.com/huozhanqiang/UBF-Net.

Keywords

• Low-light image enhancement
• unsupervised learning
• image fusion
• noise suppression