  In this paper we have present an improved Cycle GAN based model for under
water image enhancement. We have utilized the cycle consistent learning
technique of the state-of-the-art Cycle GAN model with modification in the loss
function in terms of depth-oriented attention which enhance the contrast of the
overall image, keeping global content, color, local texture, and style
information intact. We trained the Cycle GAN model with the modified loss
functions on the benchmarked Enhancing Underwater Visual Perception (EUPV)
dataset a large dataset including paired and unpaired sets of underwater images
(poor and good quality) taken with seven distinct cameras in a range of
visibility situation during research on ocean exploration and human-robot
cooperation. In addition, we perform qualitative and quantitative evaluation
which supports the given technique applied and provided a better contrast
enhancement model of underwater imagery. More significantly, the upgraded
images provide better results from conventional models and further for under
water navigation, pose estimation, saliency prediction, object detection and
tracking. The results validate the appropriateness of the model for autonomous
underwater vehicles (AUV) in visual navigation.
