为降低无人机硬件设备升级成本,研究利用深度学习技术进行航拍图像超分辨(super-resolution,SR)。针对神经网络训练参数量大的特点,提出了一种稀疏卷积神经网络SR(SR based on sparse convolutional neural network,SRSCNN)重构方法,对...为降低无人机硬件设备升级成本,研究利用深度学习技术进行航拍图像超分辨(super-resolution,SR)。针对神经网络训练参数量大的特点,提出了一种稀疏卷积神经网络SR(SR based on sparse convolutional neural network,SRSCNN)重构方法,对神经网络连接权值进行选择性筛选达到压缩网络结构并减少训练时间的目的。实验结果表明,该方法在缩短网络学习时间,图像重构效果和计算时间上具有一定优越性。同时,设计了一种基于显著性区域的图像质量评价方式,更适应航拍图像后续处理工作。展开更多
In order to improve the detail preservation and target information integrity of different sensor fusion images,an image fusion method of different sensors based on non-subsampling contourlet transform(NSCT)and GoogLeN...In order to improve the detail preservation and target information integrity of different sensor fusion images,an image fusion method of different sensors based on non-subsampling contourlet transform(NSCT)and GoogLeNet neural network model is proposed. First,the different sensors images,i. e.,infrared and visible images,are transformed by NSCT to obtain a low frequency sub-band and a series of high frequency sub-bands respectively.Then,the high frequency sub-bands are fused with the max regional energy selection strategy,the low frequency subbands are input into GoogLeNet neural network model to extract feature maps,and the fusion weight matrices are adaptively calculated from the feature maps. Next,the fused low frequency sub-band is obtained with weighted summation. Finally,the fused image is obtained by inverse NSCT. The experimental results demonstrate that the proposed method improves the image visual effect and achieves better performance in both edge retention and mutual information.展开更多
文摘为降低无人机硬件设备升级成本,研究利用深度学习技术进行航拍图像超分辨(super-resolution,SR)。针对神经网络训练参数量大的特点,提出了一种稀疏卷积神经网络SR(SR based on sparse convolutional neural network,SRSCNN)重构方法,对神经网络连接权值进行选择性筛选达到压缩网络结构并减少训练时间的目的。实验结果表明,该方法在缩短网络学习时间,图像重构效果和计算时间上具有一定优越性。同时,设计了一种基于显著性区域的图像质量评价方式,更适应航拍图像后续处理工作。
基金supported by the National Natural Science Foundation of China(No.61301211)the China Scholarship Council(No.201906835017)
文摘In order to improve the detail preservation and target information integrity of different sensor fusion images,an image fusion method of different sensors based on non-subsampling contourlet transform(NSCT)and GoogLeNet neural network model is proposed. First,the different sensors images,i. e.,infrared and visible images,are transformed by NSCT to obtain a low frequency sub-band and a series of high frequency sub-bands respectively.Then,the high frequency sub-bands are fused with the max regional energy selection strategy,the low frequency subbands are input into GoogLeNet neural network model to extract feature maps,and the fusion weight matrices are adaptively calculated from the feature maps. Next,the fused low frequency sub-band is obtained with weighted summation. Finally,the fused image is obtained by inverse NSCT. The experimental results demonstrate that the proposed method improves the image visual effect and achieves better performance in both edge retention and mutual information.
