A fast and effective U-Net video dehazing network

With ongoing research and development in deep learning technologies, an increasing array of deep learning techniques are being applied in single image and video dehazing. This paper argues that it is essential to achieve faster performance while maintaining frame coherence and overall accuracy. Therefore, this paper introduces a new video dehazing network. This network first constructs a Wavelet U-Net to learn the image’s periphery features and use Wavelet part to trains the network. Subsequently, this paper incorporates the MPG and MSR modules into the network. The MPG module ensures coherence in dehazing effects, while the MSR module overall makes the restored images visually closer to reality. Our neural network, combining these three parts, performs better than most methods and is one of the most suitable methods for certain scenarios.