Dehazing algorithm for unmanned surface vessel based on GAN-U-Net++ network

Abstract

The autonomous perception of unmanned surface vessels in the natural sea environment has improved significantly; however, it is adversely affected by haze. Improving the U-Net network can retain and restore spatial information effectively and improve image dehazing. However, many dehazing algorithms that use the U-Net network as the main architecture face problems, such as distorted images and blurred contours of small distant objects. To overcome these limitations, we propose a generative adversarial network (GAN)-U-Net++ network that uses the U-Net++ network with a coordinate attention mechanism as the main part of a GAN generator. We used the coordinate attention mechanism to perform horizontal and vertical pixel level pooling to improve the attention on small targets in hazy images. The addition of the edge preservation loss function and image gradient loss function to small targets improved the sharpness of their edges. A Markov discriminator was used to evaluate the authenticity of the images. Several experiments were conducted to compare the proposed method with existing dehazing algorithms. The structural similarity value and peak signal-to-noise ratio increased by 15.74% and 29.14%, respectively.