Underwater Image Enhancement Method Based on Feature Fusion Neural Network

Abstract

Aiming at the problems of uneven illumination of underwater image caused by supplementary illumination in deep-sea and night waters, image noise, low contrast and color deviation caused by suspended particles in water, a new underwater image enhancement method under non-uniform illumination is proposed. The heterogeneous feature fusion module is designed to fuse different levels and different levels of features, so as to improve the overall perception ability of the network to detail information and semantic information. Secondly, a new feature attention mechanism is designed to improve the traditional channel attention mechanism, and the improved channel attention and pixel attention mechanism are added to the heterogeneous feature fusion process to strengthen the ability of the network to extract pixel features with different turbidity. Then, the dynamic feature enhancement module is designed to adaptively expand the receptive field to improve the adaptability of the network to the image distortion scene and the ability of model conversion, and strengthen the network’s learning of the region of interest. Finally, the color loss function is designed, and the absolute error loss and structural similarity loss are jointly minimized to correct the color deviation on the basis of maintaining the image texture. A multi-scale feature extraction module is designed to extract different levels of features at the beginning of the network, and the output results are obtained through the convolution layer with jump connection and the attention module. The experimental results on several data sets show that this method can have good results in processing synthetic underwater images and real underwater images, and can better restore the image color and texture details compared with the existing methods. It conforms to the characteristics of human vision, and the visual effect is better than the existing underwater image enhancement algorithms.