Image Dehazing Using Residual-Based Deep CNN

There is a series of image degradation in the image acquired in haze and other weather. The single image dehazing is a challenging and ill-posed problem. Using deep neural network methods, it solves the drawbacks of manually designing haze-related features. This paper proposes a dehazing algorithm using residual-based deep CNN. The network model is divided into two phases: in the first stage, a haze image is input, and the transmission map is estimated by network; in the second stage, the ratio of foggy image and transmission map is used as input, and the residual network is used to remove haze. It avoids the estimation of atmospheric light and improves the efficiency of dehazing. To train the proposed network, we use the NYU2 depth dataset as the training set. In the full-reference metric peak signal to noise ratio, structural similarity, and feature similarity and no-reference metric Spatial–Spectral Entropy-based Quality, Blind/Referenceless Image Spatial Quality Evaluator, and Natural Image Quality Evaluator aspect, the experimental results confirm the efficiency and robustness of the proposed method.