DPAFD-net: A dual-path adaptive fusion dehazing network.

• A two-branch end-to-end dehazing network is proposed, which can solve the imbalances weight distribution problem between the detail and structural features. • Coordinate channel attention module is employed to extract the structural features and the dense pixel attention modules is employed to extract the detail features. • Extensive experiments are conducted on both synthetic and real-world dataset and performed well. Image dehazing is an ill-posed problem that has been extensively studied in recent years. Unfortunately, most existing deep dehazing models have high computational complexity and lack the dynamic adjustment of details, which hinders their application to high-resolution images in computational vision tasks. In this paper, we propose an efficient dual-path adaptive fusion dehazing network (DPAFD-Net) to directly restore a clear image from a hazy input. Moreover, we propose a pure subnetwork with encoder and decoder structures to further extract the structural information and progressively restore the haze-free image. To evaluate the effectiveness of the proposed method, we validate our approach on synthetic and real hazy images, where our method performs favourably against the state-of-the-art dehazing approaches. [ABSTRACT FROM AUTHOR]