RYF-Net: deep fusion network for single image haze removal

Abstract

Haze removal from a single image is a challenging task. Estimation of accurate scene transmission map (TrMap) is the key to reconstruct the haze-free scene. In this paper, we propose a convolutional neural network based architecture to estimate the TrMap of the hazy scene. The proposed network takes the hazy image as an input and extracts the haze relevant features using proposed RNet and YNet through RGB and YCbCr color spaces respectively and generates two TrMaps. Further, we propose a novel TrMap fusion network (FNet) to integrate two TrMaPs and estimate robust TrMap for the hazy scene. To analyze the robustness of FNet, we tested it on combinations of TrMaps obtained from existing state-of-the-art methods. Performance evaluation of the proposed approach has been carried out using the structural similarity index, mean square error and peak signal to noise ratio. We conduct experiments on five datasets namely: D-HAZY, Imagenet, Indoor SOTS, HazeRD and set of real-world hazy images. Performance analysis shows that the proposed approach outperforms the existing state-of-theart methods for single image dehazing. Further, we extended our work to address high-level vision task such as object detection in hazy scenes. It is observed that there is a significant improvement in accurate object detection in hazy scenes using proposed approach.