Wu Jiangyan, Zhang Guanghui, Fan Yugang
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Yunnan Key Laboratory of Intelligent Control and Application, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel). 2024 Nov 21;24(23):7425. doi: 10.3390/s24237425.
The underwater imaging process is often hindered by high noise levels, blurring, and color distortion due to light scattering, absorption, and suspended particles in the water. To address the challenges of image enhancement in complex underwater environments, this paper proposes an underwater image color correction and detail enhancement model based on an improved Cycle-consistent Generative Adversarial Network (CycleGAN), named LPIPS-MAFA CycleGAN (LM-CycleGAN). The model integrates a Multi-scale Adaptive Fusion Attention (MAFA) mechanism into the generator architecture to enhance its ability to perceive image details. At the same time, the Learned Perceptual Image Patch Similarity (LPIPS) is introduced into the loss function to make the training process more focused on the structural information of the image. Experiments conducted on the public datasets UIEB and EUVP demonstrate that LM-CycleGAN achieves significant improvements in Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Average Gradient (AG), Underwater Color Image Quality Evaluation (UCIQE), and Underwater Image Quality Measure (UIQM). Moreover, the model excels in color correction and fidelity, successfully avoiding issues such as red checkerboard artifacts and blurred edge details commonly observed in reconstructed images generated by traditional CycleGAN approaches.
水下成像过程常常受到高噪声水平、模糊以及由于水中光散射、吸收和悬浮颗粒导致的颜色失真的阻碍。为了解决复杂水下环境中图像增强的挑战，本文提出了一种基于改进的循环一致生成对抗网络（CycleGAN）的水下图像颜色校正和细节增强模型，名为LPIPS-MAFA CycleGAN（LM-CycleGAN）。该模型将多尺度自适应融合注意力（MAFA）机制集成到生成器架构中，以增强其感知图像细节的能力。同时，将学习到的感知图像块相似性（LPIPS）引入损失函数，使训练过程更专注于图像的结构信息。在公共数据集UIEB和EUVP上进行的实验表明，LM-CycleGAN在结构相似性指数（SSIM）、峰值信噪比（PSNR）、平均梯度（AG）、水下彩色图像质量评估（UCIQE）和水下图像质量度量（UIQM）方面取得了显著改进。此外，该模型在颜色校正和保真度方面表现出色，成功避免了传统CycleGAN方法生成的重建图像中常见的红色棋盘格伪影和边缘细节模糊等问题。
