LIGHT-DCSFN: A Light Cross-scale Fusion Network for Single Image Deahzing
Wensheng Han, Dengyin Zhang, Xiaofei Jin · 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · 2022
At present, the image dehazing algorithm based on deep learning has many problems such as multiple model parameters and taking a long time for single image dehazing. Based on this, a fast image dehazing method based on depth cross scale fusion network is proposed. First, depth separable convolution is used to replace the normal standard convolution in the original network to improve single image dehazing efficiency. Secondly, a color feature extraction module is added on the basis of the original network to make the improved network better for image dehazing. Finally, this paper uses the network to predict the haze concentration map of the input image instead of directly obtaining the haze-free image through the network, so that the final image predicted by the network is clearer and more natural. The results indicate that the proposed algorithm greatly improves the dehazing time of a single image and can dehazing in most scenes.