Deep learning-based recurrent neural network for underwater image enhancement

Factors such as scattering and absorption of light by suspended particles and lack of light in deep water exist in complex underwater environments, leading to visual degradation effects such as loss of underwater image features, colour deviation and contrast reduction. With the development of artificial intelligence, deep neural networks are widely used in the field of computer vision and show their powerful brain-like separation (local information processing) and integration (global information processing) processing capabilities. In this paper, we use the visual saliency model to construct a Gaussian pyramid of luminance, orientation, edge and colour applicable to underwater degraded images to obtain shallow image features of underwater images. Combined with the VGG16 convolutional neural network model to construct a progressive enhancement neural network based on deep learning, which in turn improves the high-dimensional saliency features of underwater degraded images. The experimental results show that the enhanced underwater image features of this algorithm have better detail retention and the colour is more in line with the human eye vision, and the experimental results of the objective indexes are better than the comparison algorithm.