LAUSR

Abstract Video monitoring environment of underwater cutting objects is rapidly deteriorated due to the increasing water turbidity in proportion to cutting operation. Precise monitoring of the cutting area is one of keys to improve work efficiency. In this paper, we studied a visibility improvement technique based on an artificial neural network for monitoring objects in a turbid underwater cutting site. In order to improve visibility in turbid water, a deep learning neural network that learned two types of visibility enhancement process was adopted. The first type is to train to restore an ideal underwater image with the best visibility from a turbid underwater image, and the second type is to train to restore an image with improved visibility using the conventional visibility improvement technique. We adopted two types of training real images based on the GAN (generative adversarial networks) model for the corresponding turbid input images. The first training image is the histogram-equalized image of a clear underwater image. If the first training image does not exist, an image with improved visibility by using histogram equalization for the turbid input image itself was used as the second training image. Experiments demonstrated that the trained neural network provided significantly improved clarity in turbid images compared to that of the conventional improving technique.