YUW-Net: A Y-shaped deep network for enhancement of Under-water images

One of the main challenges in underwater imaging is the poor quality caused by the underwater propagation of light. In this work, this distortion is analyzed separately along the color and illumination channels, and accordingly a novel Convolution neural network (CNN) architecture is proposed for the restoration of underwater images. The proposed method is independent of any approximated physical model or parameter-based model for underwater image formation. In the proposed approach the distortion is perceived as changes in the color and illumination distribution of the image, majorly in terms of the first and second moments. Extensive experimentation are done for evaluating the proposed network over real underwater images alone and in combination with the synthetically generated images. To prove the claims, a thorough comparison with the state-of-the-art works is presented, in qualitative as well as quantitative manner. The proposed network is found to outperform the state-of-the-art methods. For easy reproducibility of the work, on acceptance, the implementation of this work will be shared in https://github.com/ChandrajitChoudhury/YUWNET.