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.
