[1]沈学利,关刘美,翟宇琦.基于卷积神经网络的颜色修正水下图像增强方法[J].计算机技术与发展,2024,34(08):42-48.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0130]
　SHEN Xue-li,GUAN Liu-mei,ZHAI Yu-qi.Color Correction Underwater Image Enhancement Method Based on Convolutional Neural Network[J].,2024,34(08):42-48.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0130]
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基于卷积神经网络的颜色修正水下图像增强方法

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年08期
页码:
42-48
栏目:
媒体计算
出版日期:
2024-08-10

文章信息/Info

Title:
Color Correction Underwater Image Enhancement Method Based on Convolutional Neural Network
文章编号:
1673-629X(2024)08-0042-07
作者:
沈学利; 关刘美; 翟宇琦
辽宁工程技术大学 软件学院,辽宁 葫芦岛 125105
Author(s):
SHEN Xue-li; GUAN Liu-mei; ZHAI Yu-qi
School of Software,Liaoning Technical University,Huludao 125105,China
关键词:
卷积神经网络; 颜色校正; 水下图像增强; 轻量化; 注意力特征融合
Keywords:
convolutional neural network; color correction; underwater image enhancement; lightweight; attention feature fusion
分类号:
TP391.41
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0130
摘要:
在水下环境中,由于光线的吸收和散射等环境问题,使得图像的颜色失真和对比度低,导致图像质量下降。 为了 提高图像的视觉效果,提出了一种基于卷积神经网络的水下图像增强算法。 首先,利用新的水下成像模型校正水下图像的色偏问题;然后,利用卷积神经网络提取修正后的图像通道特征,通过多尺度注意力模块重新加权通道特征以增强不同特征图的一致性,并与颜色校正后的图像进行特征融合;最后,通过重建计算模块融合特征,改进图像增强效果。 实验结果表明,该算法能够更好地校正图像颜色失真,提高图像对比度,最主要的优势在于该算法的运行速度比其他先进的水下图像增强方法快两倍。
Abstract:
In the underwater environment,due to environmental problems such as light absorption and scattering,the color distortion and contrast of the image are low,resulting in a decrease in image quality. In order to improve the visual effect of the image,an underwater image enhancement algorithm based on convolutional neural network is proposed. Firstly,a new underwater imaging model is used to correct the color deviation of underwater images. Then,the convolutional neural network is used to extract the corrected image channel features,and the channel features are re-weighted by the multi-scale attention module to enhance the consistency of different feature maps,and feature fusion is performed with the color - corrected image. Finally, the feature is fused by the reconstruction calculation module to improve the image enhancement effect. The experimental results show that the proposed algorithm can better correct the image color distortion and improve the image contrast. The main advantage is that the running speed of the proposed algorithm is twice faster than that of other advanced underwater image enhancement methods.

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更新日期/Last Update: 2024-08-10