Learning an ensemble dehazing network for visible remote sensing images

Image dehazing is an important preprocessing task since haze extremely degrades the image quality and hampers the application of remote sensing vision system. Although the deep learning-based method has been successful in image dehazing, there has been little effort to harmonize convolutional neural networks and transformer to better satisfy removing haze. In particular, local and global representation learning are equally important for the challenging image dehazing task. To this end, we propose an effective ensemble dehazing network (EDHN) for visible remote sensing images. Specifically, we introduce two key backbone modules for the developed ensemble framework, including operation-wise attention module and transformer module. The operation-wise attention module is designed for restoring spatially varying degradation, and the transformer module is employed to refine haze-free background textures and structures. Furthermore, residue channel prior and feature aggregation block are also incorporated into our ensemble architecture to further guide image reconstruction and boost image restoration. Experimental results show the superiority of our proposed EDHN and demonstrate the favorable performance against recent dehazing approaches.