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.
稀疏表示的分块处理破环了图像的连续性,导致多聚焦融合图像的清晰测度信息严重丢失.针对上述问题,提出了卷积稀疏表示和邻域特征结合的多聚焦图像融合算法.该算法将非下采样轮廓波变换(NSCT)域低频子图通过高斯滤波分解成基础层和细节层,然后选用交替方向乘子算法(ADMM)求解稀疏系数,完成细节层特征响应系数的融合.同时,根据聚焦程度测量函数设计了合理的邻域特征,完成了NSCT域高频子图的融合.实验结果表明:该算法边缘信息传递因子(QAB/F)指标略低于对比算法,但空间频率(SF)、平均梯度(AG)、清晰度(SP)以及视觉信息保真度(VIFF)指标相比于对比算法分别提高了约16.31％、41.87％、19.2％以及12.07％,有效地提取了源图像更深层次的清晰测度信息,克服了稀疏表示的块效应缺陷.
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.
