Underwater Image Enhancement Using Dual Convolutional Neural Network with Skip Connections

Abstract

Underwater images in high quality are important for many applications but they are often in poor quality since they suffer from fog, low brightness, colour distortion, and reduced contrast. Underwater image quality is degraded with the depth of the water since the red light is absorbed more than blue and green lights and the light is scattered by the suspended particles. Although several traditional and deep learning based approaches are proposed to enhance and restore the image, producing a high quality enhanced image with natural colour is still challenging. In this paper, a novel convolutional neural network architecture is proposed and it has two identical branches to input a raw degraded image and a colour balanced image. Dense blocks are utilized to train the model with fewer parameters. In addition, skip connections are introduced over the dense blocks to preserve the spatial information. The proposed approach is evaluated on publicly available UIEB dataset and shows 28.67 of PSNR value, and 0.89 of SSIM index, which are better than the state-of-the-art approaches.