College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China.
Ningbo Institute of Oceanography, Ningbo 315832, China.
Sensors (Basel). 2023 Jun 21;23(13):5774. doi: 10.3390/s23135774.
This paper presents an efficient underwater image enhancement method, named ECO-GAN, to address the challenges of color distortion, low contrast, and motion blur in underwater robot photography. The proposed method is built upon a preprocessing framework using a generative adversarial network. ECO-GAN incorporates a convolutional neural network that specifically targets three underwater issues: motion blur, low brightness, and color deviation. To optimize computation and inference speed, an encoder is employed to extract features, whereas different enhancement tasks are handled by dedicated decoders. Moreover, ECO-GAN employs cross-stage fusion modules between the decoders to strengthen the connection and enhance the quality of output images. The model is trained using supervised learning with paired datasets, enabling blind image enhancement without additional physical knowledge or prior information. Experimental results demonstrate that ECO-GAN effectively achieves denoising, deblurring, and color deviation removal simultaneously. Compared with methods relying on individual modules or simple combinations of multiple modules, our proposed method achieves superior underwater image enhancement and offers the flexibility for expansion into multiple underwater image enhancement functions.
本文提出了一种高效的水下图像增强方法，名为 ECO-GAN，旨在解决水下机器人摄影中存在的颜色失真、对比度低和运动模糊等挑战。所提出的方法基于使用生成对抗网络的预处理框架。ECO-GAN 采用了卷积神经网络，专门针对三个水下问题：运动模糊、低亮度和颜色偏差。为了优化计算和推理速度，使用编码器提取特征，而不同的增强任务则由专用解码器处理。此外，ECO-GAN 在解码器之间使用跨阶段融合模块来加强连接并提高输出图像的质量。该模型使用带配对数据集的监督学习进行训练，实现了无需额外物理知识或先验信息的盲图像增强。实验结果表明，ECO-GAN 可以有效地同时实现去噪、去模糊和颜色偏差去除。与依赖于单个模块或多个模块简单组合的方法相比，我们提出的方法实现了更好的水下图像增强效果，并具有扩展到多个水下图像增强功能的灵活性。
