Liu Botao, Yang Yimin, Zhao Ming, Hu Min
School of Computer Science, Yangtze University, Jingzhou 434025, China.
Western Research Institute, Yangtze University, Karamay 834000, China.
Sensors (Basel). 2024 May 11;24(10):3070. doi: 10.3390/s24103070.
Underwater images suffer from low contrast and color distortion. In order to improve the quality of underwater images and reduce storage and computational resources, this paper proposes a lightweight model Rep-UWnet to enhance underwater images. The model consists of a fully connected convolutional network and three densely connected RepConv blocks in series, with the input images connected to the output of each block with a Skip connection. First, the original underwater image is subjected to feature extraction by the SimSPPF module and is processed through feature summation with the original one to be produced as the input image. Then, the first convolutional layer with a kernel size of 3 × 3, generates 64 feature maps, and the multi-scale hybrid convolutional attention module enhances the useful features by reweighting the features of different channels. Second, three RepConv blocks are connected to reduce the number of parameters in extracting features and increase the test speed. Finally, a convolutional layer with 3 kernels generates enhanced underwater images. Our method reduces the number of parameters from 2.7 M to 0.45 M (around 83% reduction) but outperforms state-of-the-art algorithms by extensive experiments. Furthermore, we demonstrate our Rep-UWnet effectively improves high-level vision tasks like edge detection and single image depth estimation. This method not only surpasses the contrast method in objective quality, but also significantly improves the contrast, colorimetry, and clarity of underwater images in subjective quality.
水下图像存在对比度低和颜色失真的问题。为了提高水下图像的质量并减少存储和计算资源，本文提出了一种轻量级模型Rep-UWnet来增强水下图像。该模型由一个全连接卷积网络和三个串联的密集连接RepConv块组成，输入图像通过跳跃连接连接到每个块的输出。首先，原始水下图像由SimSPPF模块进行特征提取，并与原始图像进行特征求和处理，以生成输入图像。然后，内核大小为3×3的第一个卷积层生成64个特征图，多尺度混合卷积注意力模块通过对不同通道的特征进行重新加权来增强有用特征。其次，连接三个RepConv块以减少特征提取中的参数数量并提高测试速度。最后，具有3个内核的卷积层生成增强后的水下图像。我们的方法将参数数量从270万个减少到45万个（减少了约83%），但通过大量实验优于现有算法。此外，我们证明了我们的Rep-UWnet有效地改进了诸如边缘检测和单图像深度估计等高阶视觉任务。该方法不仅在客观质量上超过了对比方法，而且在主观质量上显著提高了水下图像的对比度、比色法和清晰度。
