摘要
为提高图像融合的清晰度,本文提出一种基于改进的稀疏表示和脉冲耦合神经网络(pulse coupled neuralnetwork,PCNN)的图像融合。利用非下采样剪切波变换(non-subsampled shearlet transform,NSST)对源图像进行分解变换,得到相应的低频子带和高频子带具有不同的信息。对于低频子带,采用改进的稀疏表示进行融合,利用K奇异值分解(K-singular value decomposition,K-SVD)算法,并对源图像进行自适应学习的多个子字典构造成联合词典。对于高频子带,则改进PCNN融合系数的选择方法,利用改进的空间频率作为神经元反馈输入来激励PCNN模型,并根据点火输出的总幅度最大的融合规则选择高频系数。最后,将融合后的低频子带和高频子带系数进行NSST逆变换,重构出融合图像。实验结果表明:该算法很好地保留了图像的边缘信息,并且得到的图像在相关的客观评价标准上也取得了良好的效果,表明了本算法的有效性。
To improve the clarity of image fusion,in this paper,we propose an image-fusion algorithm based on im-proved sparse representation and a pulse-coupled neural network(PCNN).First,using a non-subsampled shearlet trans-form(NSST),source images are decomposed into low-frequency and high-frequency sub-band coefficients,which con-tain different information.Then,we use the K-singular value decomposition algorithm to fuse the improved sparse rep-resentation with low-frequency sub-band coefficients and construct a joint dictionary from the adaptive learning mul-tiple sub-dictionaries in the source images.The high-frequency sub-band coefficients are fused with the improved PCNN.To stimulate the PCNN model,we use the modified spatial frequency as neuron feedback input.The high-fre-quency coefficients are selected according to the fusion rule for the maximum amplitude of fire output.Finally,we re-construct the fused image with the NSST inverse transform of the fused low-frequency and high-frequency sub-band coefficients.The experimental results show that the proposed algorithm preserves the edge information of the source im-ages very well;additionally,the fused image achieves good results on the evaluation criteria,thus verifying the effect-iveness of the proposed algorithm.
作者
王建
吴锡生
WANG Jian;WU Xisheng(School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China)
出处
《智能系统学报》
CSCD
北大核心
2019年第5期922-928,共7页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金项目(61672265)
