The fusion of the low-frequency subband in the non-subsampled shearlet transform(NSST)domain requires artificially obtained fusion modes;thus,the spatial continuity and contour detail information of the source image are not adequately captured.An infrared and visible image fusion algorithm based on a convolutional neural network is proposed to solve this problem.First,the Siamese convolutional neural network is used to learn the characteristics of the low-frequency subband in the NSST domain and output a feature map that measures the spatial detail information of the subbands.Then,on the basis of the feature map obtained by Gaussian filter processing,a local-similarity-based measurement function is designed to adaptively adjust the fusion mode of the low-frequency subband in the NSST domain.Finally,on the basis of the variance of the high-frequency subband in the NSST domain,the local region energy,and the visibility characteristics,the pulse-coupled neural network(PCNN)parameters are adaptively set to complete the fusion of the high-frequency subband in the NSST domain.Experimental results show that the Q^AB/F index of the algorithm is slightly lower than that of the comparison algorithm.However,the spatial frequency,SP,structural similarity,and visual information fidelity for fusion are improved by approximately 50.42%,14.25%,7.91%,and 61.67%,respectively,which indicates that the method effectively solves the low-frequency subband fusion mode.It also eliminates the need to manually set the PCNN parameters to solve this problem.

高校学生资助政策是促进大学生发展,保障教育公平的一项重大工程,对建设人力资源强国,构建和谐社会具有重要的意义。本文通过对高校资助工作基本内容及政策发展演变进行梳理,对当前高校资助工作存在的问题及原因进行简要分析,提出下一步做好高校资助工作的意见建议,旨在增强高校资助工作的针对性和实效性,落实资助育人工作成效。

稀疏表示是以块为单位进行编码的,因此破坏了图像块间的相关性.针对上述问题,提出了基于卷积稀疏表示的红外与可见光图像融合算法.该算法采用交替方向乘子算法(ADMM)求解非下采样轮廓波变换(NSCT)域强边缘子带的卷积稀疏系数,完成特征响应系数的融合.同时,采用脉冲耦合神经网络(PCNN)模型的点火图完成NSCT域高频子带的融合.实验结果表明:该算法解决了稀疏表示的“块效应”问题,同时又兼具PCNN模型的视觉特性,可以有效地捕捉源图像的特征信息.另外,在主观视觉评价和客观质量评价方面均优于现有算法.