CDNet: Single Image De-Hazing Using Unpaired Adversarial Training

Outdoor scene images generally undergo visibility degradation in presence of aerosol particles such as haze, fog and smoke. The reason behind this is, aerosol particles scatter the light rays reflected from the object surface and thus results in attenuation of light intensity. Effect of haze is inversely proportional to the transmission coefficient of the scene point. Thus, estimation of accurate transmission map (TrMap) is a key step to reconstruct the haze-free scene. Previous methods used various assumptions/priors to estimate the scene TrMap. Also, available end-to-end dehazing approaches make use of supervised training to anticipate the TrMap on synthetically generated paired hazy images. Despite the success of previous approaches, they fail in real-world extreme vague conditions due to unavailability of the real-world hazy image pairs for training the network. Thus, in this paper, Cycle-consistent generative adversarial network for single image De-hazing named as CDNet is proposed which is trained in an unpaired manner on real-world hazy image dataset. Generator network of CDNet comprises of encoder-decoder architecture which aims to estimate the object level TrMap followed by optical model to recover the haze-free scene. We conduct experiments on four datasets namely: D-HAZY 1, Imagenet 5, SOTS 20 and real-world images. Structural similarity index, peak signal to noise ratio and CIEDE2000 metric are used to evaluate the performance of the proposed CDNet. Experiments on benchmark datasets show that the proposed CDNet outperforms the existing state-of-the-art methods for single image haze removal.