  When sailing at sea, the smart ship will inevitably produce swaying motion
due to the action of wind, wave and current, which makes the image collected by
the visual sensor appear motion blur. This will have an adverse effect on the
object detection algorithm based on the vision sensor, thereby affect the
navigation safety of the smart ship. In order to remove the motion blur in the
images during the navigation of the smart ship, we propose SharpGAN, a new
image deblurring method based on the generative adversarial network. First of
all, the Receptive Field Block Net (RFBNet) is introduced to the deblurring
network to strengthen the network's ability to extract the features of blurred
image. Secondly, we propose a feature loss that combines different levels of
image features to guide the network to perform higher-quality deblurring and
improve the feature similarity between the restored images and the sharp image.
Finally, we propose to use the lightweight RFB-s module to improve the
real-time performance of deblurring network. Compared with the existing
deblurring methods on large-scale real sea image datasets and large-scale
deblurring datasets, the proposed method not only has better deblurring
performance in visual perception and quantitative criteria, but also has higher
deblurring efficiency.
