UAV Remote Sensing Image Dehazing Based on Multi-Dimensional Saliency Awareness Unequal Network

Current UAV image haze removal methods often suffer from problems of insufficient dehazing and spectrum distortion, especially in regions with rich spectrum and texture information. In this paper, we propose a multi-dimensional saliency awareness unequal network to avoid texture loss and color distortions. First, we design an unequal network structure, which enhances local color and texture detail feature learning. Specifically, we propose a saliency dense block, which employs a saliency map to guide the network to pay attention to the regions with rich spectrum and texture information unequally. Second, a multi-dimensional saliency detection method is proposed. It fuses the features of different dimensions to extract the salient regions, to obtain the spectrum and texture information. Finally, a loss function combining mean square error and edge loss is defined to enhance the learning of the texture. The experimental results demonstrate that the proposed method is capable of restoring color information as well as reserving texture details, especially for salient regions of UAV hazy images.