计算机科学
水下
人工智能
图像质量
计算机视觉
极化(电化学)
人工神经网络
图像分辨率
旋光法
遥感
光学
图像(数学)
地质学
物理
散射
海洋学
化学
物理化学
作者
Liying Lang,Haoran Feng,Jinghan Zhang,Yajun Pang
出处
期刊:Applied Optics
[The Optical Society]
日期:2024-02-14
卷期号:63 (6): 1590-1590
摘要
The polarization imaging technique leverages the disparity between target and background polarization information to mitigate the impact of backward scattered light, thereby enhancing image quality. However, the imaging model of this method exhibits limitations in extracting inter-image features, resulting in less-than-optimal outcomes in turbid underwater environments. In recent years, machine learning methodologies, particularly neural networks, have gained traction. These networks, renowned for their superior fitting capabilities, can effectively extract information from multiple images. The incorporation of an attention mechanism significantly augments the capacity of neural networks to extract inter-image correlation attributes, thereby mitigating the constraints of polarization imaging methods to a certain degree. To enhance the efficacy of polarization imaging in complex underwater environments, this paper introduces a super-resolution network with an integrated attention mechanism, termed as SRGAN-DP. This network is a fusion of an enhanced SRGAN network and the high-performance deep pyramidal split attention (DPSA) module, also proposed in this paper. SRGAN-DP is employed to perform high-resolution reconstruction of the underwater polarimetric image dataset, constructed specifically for this study. A comparative analysis with existing algorithms demonstrates that our proposed algorithm not only produces superior images but also exhibits robust performance in real-world environments.
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