Coarse-to-fine multi-scale attention-guided network for multi-exposure image fusion

In recent years, deep learning networks have achieved prominent success in the field of multi-exposure image fusion. However, it is still challenging to prevent color distortion and blurry edges which leads to bad visual effects. In this paper, we present a multi-scale attention-guided network for multi-exposure image fusion in a coarse-to-fine manner. The network generates multi-scale enhanced attention weight maps of images in different sizes which possess vital details and can emphasize essential regions of interest from both sides. The multi-scale structure can extract features on different scales, and the bilayer structure can extract features from different image sizes. Moreover, we designed a coarse-to-fine attention module to finally generate the weight maps; the module combines channel attention with spatial attention. Fused results will be generated under the guidance of the weight maps. Qualitative and quantitative experiments are performed on a publicly available dataset which shows our method outperforms the state-of-the-art methods in visual effect and objective analysis. Also, ablation experiments prove each part of our method has a great advantage in generating images with significant details, prominent targets, and faithful color.