DHD-Net: A Novel Deep-Learning-based Dehazing Network

Abstract

Eliminating haze interference in images is still a challenging problem. In this paper, we consider more systematically the physical hazing mechanisms, combined with deep learning, propose a new end-to-end dehazing network called DHD-Net. For physical hazing mechanisms, we fuse the global atmosphere light, transmission maps, and the atmospheric scattering model for dehazing. For the estimation of global atmosphere light, We propose a deep learning-based haze density estimation algorithm (DL-HDE). We establish a new dataset, of which each data item consists of the hazy image, the transmission map, the haze-free image, and the dense-haze area mask. Our experimental results demonstrate that our proposed DHD-Net has better dehazing performance than state-of-the-art algorithms.

Using Haze Level Estimation in Data Cleaning for Supervised Deep Image Dehazing Models

“…The existing deep learning methods in image dehazing can be divided into two categories: model-based methods and end-to-end methods. Model-based methods use deep learning to estimate the model parameters A or t(x) or both, based on Equation (1), such as [6][7][8][9][10][11][12]. End-to-end methods, which are currently more popular in the image dehazing community, directly learn the mapping between hazy and ground truth (GT) images from a training data set.…”