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一个基于CNN的视觉转换器用于水下图像增强ViTClarityNet

Mohamed E Fathy¹, Samer A Mohamed^2,3, Mohammed I Awad²

¹Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, 11535, Egypt. 2002597@eng.asu.edu.eg.

Scientific reports

|May 14, 2025

概括

本研究介绍了ViT-Clarity,这是一个使用视觉变压器和CNN的先进的水下图像增强模块. 它有效地改善了水下计算机视觉任务,如物体检测,解决了可见性差和散射的挑战.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
海洋学海洋学海洋学

背景情况:

水下环境对计算机视觉提出了重大挑战,原因是光散射,吸收和光照不佳.
现有的方法难以有效地增强水下图像,限制了各种视觉任务的执行.

研究的目的:

开发和评估一个有效的水下图像增强模块,ViT-Clarity,利用视觉变换器.
为了解决配对的水下数据集的稀缺性,提出了一种合成数据生成方法,BlueStyleGAN.
在下游计算机视觉应用中展示增强水下图像的实际实用性.

主要方法:

介绍了ViT-Clarity,这是一个水下图像增强模块,将视觉变压器 (ViT) 与卷积神经网络 (CNN) 集成在一起.
开发了BlueStyleGAN,一个生成对抗网络 (GAN),从清晰的空中图像中创建现实的合成水下图像.
在使用定量 (UCIQM, UCIQE, URanker) 和定性指标的五个不同的水下数据集上评估了ViT-ClarityNet,并将其与最先进的方法和无变压器变体 (ClarityNet) 进行比较.

主要成果:

与现有方法和Clarity.Net相比,ViT-ClarityNet在水下图像增强方面表现出卓越的性能.
BlueStyleGAN在生成现实的合成水下图像方面被证明是有效的,显示出良好的训练稳定性.
来自ViT-Clarity的增强图像显著提高了对象检测和SIFT特征匹配任务的性能.

结论:

ViT-Clarity为水下图像增强提供了强大的解决方案,显著提高了可见性,并使计算机视觉更有效.
视觉变压器的集成对于在水下图像恢复中实现最先进的性能至关重要.
拟议的BlueStyleGAN解决了数据稀缺问题,促进了水下视觉的进一步研究和开发.

关键词:

卷积神经网络是一种卷积神经网络.产生性的对抗性对抗性.综合数据集是一种合成数据集.水下图像增强水下图像增强视觉变压器视觉变压器

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