Underwater Image Enhancement Algorithm Combining Transformer and Generative Adversarial Network

Owing to the diversity of underwater environments and the scattering and selective absorption of light in water, acquired underwater images usually suffer from severe quality degradation problems, such as color deviation, low clarity, and low brightness. To solve these problems, an underwater image enhancement algorithm that combines a transformer and generative adversarial network is proposed. Based on the generative adversarial network, a generative adversarial network with transformer (TGAN) network enhancement model is constructed by combining the coding and decoding structure, global feature modeling transformer module based on the spatial self-attention mechanism, and channel-level multi-scale feature fusion transformer module. The model focuses on color and spatial channels with more serious underwater image attenuation. This effectively enhances the image details and solves the color-deviation problem. Additionally, a multinomial loss function, combining RGB and LAB color spaces, is designed to constrain the adversarial training of the network enhancement model. The experimental results demonstrate that when compared to typical underwater image enhancement algorithms, such as contrast-limited adaptive histogram equalization (CLAHE), underwater dark channel prior (UDCP), underwater based on convolutional neural network (UWCNN), and fast underwater image enhancement for improved visual perception (FUnIE-GAN), the proposed algorithm can significantly improve the clarity, detail texture, and color performance of underwater images. Specifically, the average values of the objective evaluation metrics, including the peak signal-to-noise ratio, structural similarity index, and underwater image quality measure, improve by 5.8%, 1.8%, and 3.6%, respectively. The proposed algorithm effectively improves the visual perception of underwater images.