电子测量与仪器学报

周露跚,赵磊,李恒,刘辉,张国银.基于轻量级密集残差网络的水下图像增强[J].电子测量与仪器学报,2023,37(1):70-77

基于轻量级密集残差网络的水下图像增强

Underwater image enhancement based on lightweight dense residual network

DOI：10.13382/j.issn.1000-7105.2023.01.008

中文关键词: 水下图像增强轻量级卷积神经网络深度可分离卷积密集连接残差学习

英文关键词:underwater image enhancement lightweight convolutional neural network depthwise separable convolution dense connection residual learning

基金项目:国家自然科学基金(61863018)、云南省科技厅面上项目(202001AT070038)资助

作者	单位
周露跚	1.昆明理工大学信息工程与自动化学院
赵磊	1.昆明理工大学信息工程与自动化学院
李恒	1.昆明理工大学信息工程与自动化学院
刘辉	1.昆明理工大学信息工程与自动化学院
张国银	1.昆明理工大学信息工程与自动化学院

Author	Institution
Zhou Lushan	1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology
Zhao Lei	1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology
Li Heng	1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology
Liu Hui	1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology
Zhang Guoyin	1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology

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中文摘要:

深度卷积神经网络是水下图像增强的主要方法之一,但其过高的内存消耗和计算需求阻碍了在实际应用中的部署。为此,提出一种轻量级的密集残差卷积神经网络( dense residual convolutional neural networks, DRCNN)用于水下图像增强。为降低计算成本,DRCNN 采用深度可分离卷积提取高级特征;通过密集连接和残差学习促进不同通道之间的信息交互,提高模型表征能力;将输入的退化图像与中间特征图融合,保留图像全局相似性,同时防止模型梯度消失。实验结果证明 DRCNN 能有效提高水下图像质量,较于现有算法,DRCNN 参数量减少了 85%,PSNR、SSIM 值分别提高了 3%、2%,测试速度提高了 3%。 DRCNN 使用更少的参数实现了更好的性能,利于在低资源设备的实时场景中应用。

英文摘要:

Deep convolutional neural networks are one of the main methods for underwater image enhancement, but their expensive memory consumption and computational requirements hinder their deployment in practical applications. To this end, a lightweight dense residual convolutional neural networks (DRCNN) is proposed for underwater image enhancement. DRCNN uses depthwise separable convolution to extract high-level features to reduce computational cost; promotes information interaction between different channels through dense connection and residual learning, but also improves model representation; and fuses the input degraded image with the intermediate feature map to preserve image global similarity while preventing model gradients from vanishing. The experimental results demonstrate that DRCNN can significantly improve the quality of underwater images. When compared to the existing algorithm, DRCNN parameters are reduced by 85%, PSNR and SSIM values are increased by 3% and 2% respectively, and test speed is improved by 3%. DRCNN achieves better performance with fewer parameters, which is advantageous for real-time applications on low-resource devices.

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