Abstract:
Degradation in the quality of images that are captured
in the hazy environment is mainlydue to 1) different weather
conditions and 2) the attenuation in reflected light. These factors
introduce a severe color distortion and low visibility in the captured
images. To tackle these problems, we propose an end-to-end trainable image de-hazing network named as LIGHT-Net. The proposed
LIGHT-Net comprises of color constancy module and haze reduction module. Among these, the color constancy module removes
the color cast added in hazy image due to the weather condition.
Whereas, the proposed haze reduction module, which is build using
an inception-residual block, is aimed to reduce the effect of haze as
well as to improve the visibility in the hazy image. Unlike traditional
feature concatenation, in the haze reduction module, we propose
a dense feature sharing to effectively share the features learned
at initial layers across the network. In general, a major hurdle to
train a convolution neural network for haze removal task is the
unavailability of large-scale real-world hazy, and corresponding
haze-free image (i.e. paired data). Thus, we make use of an unpaired
training approach to train the proposed LIGHT-Net for image
de-hazing. Extensive analysis has been carried out to validate the
necessity and impact of each sub-block of the proposed LIGHT-Net.
A large set of real-world hazy images captured in different weather
conditions are considered to validate the proposed approach for image de-hazing. Also, the benchmark synthetic hazy image database
is considered for a quantitative analysis of the proposed LIGHTNet
for image de-hazing. Further, we have shown the usefulness of the
proposed LIGHT-Net for underwater image enhancement. Experiments show that the proposed LIGHT-Net outperforms the other
existing approaches for both image de-hazing as well as underwater
image enhancement.
