舰船图像增强对目标图像特征提取具有非常重要的意义。本文研究雾天图像成像模型，设计基于卷积神经网络的雾天舰船图像增强的算法流程，包括图像数据准备、卷积神经网络模型构建、模型训练以及图像增强等4个阶段，最后对舰船图像增强前后效果进行对比，雾天船舶图像背景和细节都得到加强，表明本文提出的基于卷积神经网络的雾天舰船图像增强方法行之有效，能够有效提升图像质量。
Ship image enhancement plays an important role in feature extraction of target image. In this paper, the image model of foggy ship is studied, and the algorithm flow of foggy ship image enhancement based on convolutional neural network is designed, which includes four stages: image data preparation, convolutional neural network model construction, model training and image enhancement. Finally, the effects before and after ship image enhancement are compared, and the background and details of ship image in foggy day are enhanced. The results show that the convolutional neural network based image enhancement method proposed in this paper is effective and can effectively improve the image quality.
2024,46(23): 160-163 收稿日期：2024-4-16
DOI：10.3404/j.issn.1672-7649.2024.23.028
分类号：U667.65
基金项目：教育部协同育人资助项目（2220602842241520）
作者简介：刘好斌(1981-),男,硕士,实验师,研究方向为图像处理及软件测试
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