Abstract:
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.
