Attention-Based Encoder-Decoder Network For Single Image Dehazing
Shunan Gao, Jinghua Zhu, Heran Xi · 2021
Single image dehazing is a challenging problem in computer vision due to it is highly ill-posed. Although recent research has made great progress, the dehazed images produced by existing models still have residual haze and lose too much detail information. To solve the above problems, we propose an end-to-end Attention-based Encoder-Decoder Network (AEDNet) which is capable to effectively remove haze while preserving image details well. AEDNet employs a novel channel shuffle attention mechanism to adaptively adjust the weight of each channel-wise feature. This attention mechanism is integrated in residual block which is the core feature extraction module of encoder-decoder. Extensive experiments on synthetic datasets and real-hazy images demonstrate that our AEDNet achieves better performance compared with previous state-of-the-art methods.