A learnable full-frequency transformer dual generative adversarial network for underwater image enhancement - Zheng, Shijian; Wang, Rujing; Zheng, Shitao; Wang, Liusan; Liu, Zhigui (Frontiers in Marine Science, 2024)
Underwater applications present unique challenges such as color deviation, noise, and low contrast, which can degrade image quality. Addressing these issues, we propose a novel approach called the Learnable Full-Frequency Transformer Dual Generative Adversarial Network (LFT-DGAN). Our method comprises several key innovations. Firstly, we introduce a reversible convolution-based image decomposition technique. This method effectively separates underwater image information into low, medium, and high-frequency domains, enabling more thorough feature extraction. Secondly, we employ image channels and spatial similarity to construct a learnable full-frequency domain transformer. This transformer facilitates interaction between different branches of information, enhancing the overall image processing capabilities. Finally, we develop a robust dual-domain discriminator capable of learning spatial and frequency domain characteristics of underwater images. Extensive experimentation demonstrates the superiority of the LFT-DGAN method over state-of-the-art techniques across multiple underwater datasets.Our approach achieves significantly improved quality and evaluation metrics, showcasing its effectiveness in addressing the challenges posed by underwater imaging. The code is visible https://github.com/zhengshijian1993/LFT-DGAN.