一个深度特征融合水下图像增强模型基于感知视觉旋转变压器
Shasha Tian1, Adisorn Sirikham1, Jessada Konpang1
1Faculty of Engineering, Rajamangala University of Technology Krungthep, Bangkok 10120, Thailand.
Journal of imaging
|January 27, 2026
概括
这项研究引入了一种新的U形框架,用于水下图像增强,显著提高对比度和颜色保真度. 新方法取得了最先进的结果,恢复了水下景观的清晰度.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 海洋技术 海洋技术
背景情况:
- 水下光学图像对于海洋勘探和监测至关重要.
- 由于散射和吸收而导致的图像退化会减少对比度和细节.
- 现有的方法与严重的水下图像扭曲作斗争.
研究的目的:
- 开发一个先进的水下图像增强框架.
- 为了应对减少对比度,色彩扭曲和细节丢失的挑战.
- 为了提高水下光学图像的质量,用于各种应用.
主要方法:
- 提出了一个U形框架,将Swin-Transformer块与注意力和剩余模块集成在一起.
- 引入了双窗口多头自动注意 (DWMSA) 为上下文和结构.
- 利用全球意识注意力图 (GAMP) 和特征增强剩余网络 (FARN) 进行自适应增强.
- 使用Charbonnier,感知和边缘损失训练模型.
主要成果:
- 在UFO-120和EUVP数据集上实现了最先进的性能.
- 报告的高平均指标:PSNR 29.5 dB,SSIM 0.94,LPIPS 0.17,UIQM 3.62,UCIQE 0.59. 这些指标均为高.
- 在对比度,色彩恢复和细节度方面表现出显著的改进.
结论:
- 拟议的框架有效地增强了受损的水下图像.
- 该方法在定量和定性评估中表现优于现有技术.
- 结果支持该框架在海洋资源勘探和监测方面的实用性.
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