一个尖端的组合模型,用于增强水下图像恢复和质量改进
1Department of Electronics and Communication Engineering, Kings Engineering College, Chennai, 602117, India. saralaabel@gmail.com.
Scientific reports
|August 19, 2025
概括
本研究介绍了一种基于集成金字塔的卷积神经网络和深通道先前除尘网络 (EPCNN-DCPDN),用于优越的水下图像增强. 这种新型模型在具有挑战性的海洋环境中有效地恢复了清晰度,颜色和细节.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 水下图像的可见性很差,颜色扭曲,由于光吸收和散射而产生雾.
- 现有的增强方法往往无法充分解决这些复杂的挑战.
研究的目的:
- 提出一个新的组合模型,EPCNN-DCPDN,用于有效的水下图像增强.
- 通过恢复颜色,对比度和细节来提高水下图像的质量.
主要方法:
- 开发了一种组合模型,将基于金字塔的CNN和深度频道先前除尘网络 (DCPDN) 结合起来.
- 实施了顺序 (DCPDN然后CNN) 和并行 (加权/学习融合) 操作模式.
- 评估了多个水下数据集的性能,与九个最先进的模型对比.
主要成果:
- EPCNN-DCPDN的PSNR达到了28.34dB,SSIM达到了0.902,UIQM达到了3.56的优异性能.
- 在浅处,深处和低光水下条件下显示了97.92%的准确性.
- 在具有挑战性的场景中表现优于WaterGAN和Haze-Line先行模型等领先模型.
结论:
- EPCNN-DCPDN模型显著提高了水下图像质量,恢复了关键的视觉信息.
- 它在各种条件下强大的性能使其适合于水下勘探,海洋研究和物体检测.
关键词:
DCPDN DCPDN 的意思消毒 消毒 消毒 消毒EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCNN-DCPDN EPCN-DCPDN-DCPDN EPCN-DCPDN-DCPDN EPCN-DN-DCPDN-DCPDN-DCPDN-DCPDN-DCPDN-DCPDN-DCPDN图像恢复 图像恢复这是一个PSNR.基于金字塔的CNN就是CNN.在SSIM中,SSIM是SSIM.在UIQM中使用UIQM.水下图像增强水下图像增强相关概念视频
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