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水下大图像增强基于CNN-变压器融合技术

Zhenggang Yang¹, Chongxin Yuan², Luyao Li¹

¹POWERCHINA GuiYang Engineering Corporation Limited, Guiyang, 550081, China.

Scientific reports

|November 12, 2025

概括

这项研究引入了增强超分辨率变压器GAN (ESRTGAN) 来改善水下大图像. 这种新型网络提高了图像质量,以准确进行安全评估.

科学领域:

水力发电工程水力发电工程
计算机视觉计算机视觉
图像处理图像处理

背景情况:

水下大安全检查需要高精度的图像分析.
水下图像遭受光学降解 (噪音,颜色变化,模糊),阻碍了缺陷检测.
现有的方法无法完全解决水下光学特征和深度学习的局限性.

研究的目的:

为水下大图像提出一个创新的图像消除和超分辨率网络.
解决传统和深度学习方法在恢复退化的水下结构图像方面的局限性.
提高大缺陷检测和安全评估的准确性.

主要方法:

开发了一个增强的超分辨率变压器GAN (ESRTGAN) 集成CNN和视觉变压器.
雇佣了多级特征融合,适应性道注意力和渐进式培训.
专注于将本地特征提取 (CNN) 与全球上下文建模 (变压器) 融合在一起.

主要成果:

通过PSNR,SSIM和LPIPS测量,ESRTGAN在真实大水下图像数据集上表现出色.
网络有效地恢复了图像质量,保留了关键的结构细节和颜色.
在满足手动解释标准的同时,实现了高计算效率.

结论:

ESRTGAN提供了一个强大的解决方案,用于增强退化的水下大图像.
该方法为长期水健康监测中的自动化分析提供了可靠的技术支持.
提高图像质量导致更准确的安全评估和缺陷检测.

关键词:

卷积神经网络 (CNN) 是一种神经网络.功能融合的功能融合生成对抗网络 (GAN) 是一种产生对抗网络.在水下图像增强.视觉变压器视觉变压器

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