EANET: Efficient Attention-Augmented Network For Real-Time Semantic Segmentation

in this paper, we propose a dynamic mutual enhancement network (DMENet) for haze removal in remote sensing images. It has three major advantages compared with other dehazing algorithms: 1) The proposed DMENet is based on the U-Net architecture to extract features effectively, which is composed of three components, i.e., a multi-scale encoder, a middle transmission layer (MTL), and a dynamic mutual decoder. 2) The dynamic mutual enhancement (DME) module is designed to dynamically integrate multi-level feature maps in a mutual way, which contains the low-level detail information and high-level semantic information respectively. 3) To improve the robustness and generalization performance of the DMENet, the hybrid supervision is built for network training between the restored results and their ground-truth labels, which consists of the pixel-level supervision, patch-level supervision and image-level supervision. Experimental results on both synthetic datasets and real remote sensing hazy images demonstrate that the proposed DMENet can gain significant progresses over the competing methods.