吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (3): 785-796.doi: 10.13229/j.cnki.jdxbgxb.20220483
• 计算机科学与技术 • 上一篇
De-xing WANG(),Kai GAO,Hong-chun YUAN(
),Yu-rui YANG,Yue WANG,Ling-dong KONG
摘要:
针对水下图像对比度低、细节表现差且存在色偏等问题,提出了一种多输入的基于TransFormer和卷积神经网络(CNN)的水下图像复原方法。利用TransFormer和相对总变差(RTV)构造深度特征提取模块,融合RTV提取的纹理图与TransFormer提取到的图像信息,有效增强了图像的细节特征。利用自动色彩均衡和Lab色彩空间构建色彩校正模块,提升图像对比度,同时校正颜色。利用多项损失函数约束网络收敛,得到增强后的清晰水下图像。最后,将本文方法与其他方法在测试集上进行定量和定性对比分析,实验结果表明,经过本文方法处理后的图像在清晰度、色彩表现和纹理信息方面均优于其他对比方法。
中图分类号:
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