A remote sensing image dehazing network based on dark channel attention mechanism

Clear and haze-free remote sensing image is crucial for subsequent processing and application. We propose a new deep learning network based on the dark channel attention mechanism to enhance remote sensing image dehazing performance by combining prior knowledge with deep learning. The network features a parallel cascade structure of attention flow and dark channel prior (DCP) constraint flow. Additionally, edge loss is introduced to supervise the training network and preserve edge information in the image content. Experiments are conducted on both real and synthetic haze datasets, demonstrating that the proposed method effectively removes non-uniformly distributed haze and produces superior results in both qualitative and quantitative analyses.