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渐进式多尺度感知网络用于非均模糊的水下图像恢复

Dechuan Kong^1,2, Yandi Zhang³, Xiaohu Zhao²

¹School of Artificial Intelligence, Henan Institute of Science and Technology, Xinxiang 453003, China.

Sensors (Basel, Switzerland)

|September 13, 2025

概括

这项研究介绍了PMSPNet,这是一个新的网络,用于增强水下图像,这些图像因不均的模糊而退化. 该方法在海洋机器人和监控等应用中显著提高了图像质量.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
机器人技术机器人技术机器人技术

背景情况:

水下成像由于流,散射和运动而遭受空间变化的模糊.
现有的增强方法在现实的水下场景中不足以解决不均的模糊.
这种限制阻碍了关键下游视觉任务的性能.

研究的目的:

开发一个先进的网络,有效地处理不均的水下模糊.
提高水下视觉系统的视觉质量和性能.
为评估不均的水下模糊恢复提供一个基准.

主要方法:

拟议的PMSPNet (进步的多尺度感知网络) 对于水下模糊不均的模糊.
集成混合交互注意模块用于特征模糊性建模.
采用了渐进式运动意识感知分支和渐进式特征反块,用于精细的模糊本地化和重建.
构建了一个多样化的非统一的水下模糊基准.

主要成果:

与多个数据集的最先进方法相比,PMSPNet在多个数据集上实现了更高的性能.
显示的平均PSNR为25.51dB.
实现了0.01秒的快速推断速度.

结论:

PMSPNet有效地解决了水下模糊不均的挑战.
该方法增强了水下机器人的视觉感知,生态监测和检查.
PMSPNet在水下图像恢复技术方面取得了重大进展.

关键词:

混合交互注意力注意力交互多个尺度的感知.水下图像增强水下图像增强在水下不均的模糊.

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