Dense Residual Fusion and Spatial Local Filtering Low Light Dehazing Algorithm Based on Attention Mechanism

Abstract

Image dehazing under low-light condition is easy to produce color distortion, patches and artifacts. To adapt to low-light condition, a dense residual fusion and spatial local filtering dehazing algorithm based on attention mechanism is proposed. Firstly, dense residual block is proposed to increase the depth of the neural network, so that the network can learn more advanced feature information. Then, the spatial and channel attention mechanism is introduced to filter and screen the features, so that the network can distinguish the uneven illumination areas and solve the problems of color distortion. The model adopts the method of spatial local filter enhancement to improve the contrast, clarity and visibility of dehazing results. Finally, a learning of joint loss function constraint network is designed to avoid error amplification and learning hybrid degradation of series structure. In the PyTorch environment, the night urban synthetic haze dataset (NHR) is used for testing and compared with the existing dehazing algorithms such as FFANet and GridDehaze. The experimental results show that compared with other dehazing algorithms, proposed method improves PSNR by 8.01‒14.16 dB and increases SSIM by 0.10‒0.36. In addition, proposed method solves the problems of color distortion, plaque and artifact.