基于多模态深度学习的特定虚拟图像视觉特征自动补偿 |
点此下载全文 |
引用本文:何伟1,杨大伟1,马天福1,马崇瑞1,苑学贺2.基于多模态深度学习的特定虚拟图像视觉特征自动补偿[J].计算技术与自动化,2024,(3):102-107 |
摘要点击次数: 68 |
全文下载次数: 0 |
|
|
中文摘要:为了提高特定虚拟图像视觉特征缺失补偿效果,提出了基于多模态深度学习的特定虚拟图像视觉特征自动补偿方法。首先,通过结合主成分分析(PCA)降维方法和结构张量的非局域全变分方法,对图像实行冗余信息去除和去噪处理;其次,通过稀疏自编码器和卷积池化技术展开深度学习,获取多模态下图像的彩色图、深度图、灰度图和3D曲面法线特征;最后,通过卷积神经网络完成特定虚拟图像视觉特征自动补偿。测试结果表明:这种特征补偿方法能够通过预处理提高图像清晰度,且图像视觉特征自动补偿质量较高。 |
中文关键词:多模态 特定虚拟图像 视觉特征自动补偿 图像去噪 卷积神经网络 |
|
Automatic Compensation of Visual Features of Specific Virtual Images Based on Multimode Depth Learning |
|
|
Abstract:In order to improve the compensation effect for missing visual features of specific virtual images, a multimodal deep learning based automatic compensation method for visual features of specific virtual images is proposed. Firstly, by combining principal component analysis (PCA) dimensionality reduction method with structural tensor non local total variation method, redundant information is removed and denoised from the image. Secondly, deep learning is carried out through sparse autoencoder and convolutional pooling techniques to obtain color, depth, grayscale, and 3D surface normal features of images under multimodal conditions. Finally, automatic compensation of specific virtual image visual features is achieved through convolutional neural networks. The test results show that this feature compensation method can improve image clarity through preprocessing, and the quality of image visual feature automatic compensation is high. |
keywords:multimode specific virtual images automatic compensation of visual features image denoising convolution neural networks |
查看全文 查看/发表评论 下载pdf阅读器 |