本文提出了一种基于红外激光干涉仪检测非球面面形的新方法,分为3个步骤:首先,利用红外激光干涉仪测量分析出非球面与标准球面之间的波像差;然后,根据非球面方程得出非球面与标准球面之间波像差的理论值;最后通过计算得出非球面的面形偏差.为验证这一方法的正确性和可靠性,采用ZYGO可见光干涉仪,使用补偿镜法测量了同一块抛物面反射镜的面形误差.结果表明,两种测量方法结果吻合.本新方法方便快捷,具有较强的通用性,可以用于非球面在加工过程中的面形测试.

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.

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.