针对非下采样剪切波变换(NSST)分解后图像的高频系数数据量较大且具有较大稀疏性的问题，本文提出一种基于 NSST 和压缩感知(CS)的图像融合算法。算法首先采用 NSST 对源图像进行分解；其次利用 CS 算法将 NSST 分解后的图像的高频系数进行压缩、融合及重构；然后利用“局部区域能量和局部区域方差”联合指导待融合图像的低频系数的融合；最后利用 NSST 逆变换重构融合图像。由于只需要对高频系数的压缩值进行融合，因此算法可以在不影响图像融合效果的同时加快代码的运行速度。仿真实验表明，该算法不需要原图像的先验知识就可以完成图像的融合，当图像的尺寸较大时，该算法牺牲了微小的融合图像质量，但却可以显著提高算法的运行速度，减小代码的时间代价，降低对硬件系统的要求。该算法对于融合系统的实时性要求提供了一种思路，具有较大的应用价值。
After the image decomposition with NSST,the high-frequency coefficients have a large amount of data and greater sparsity.In order to obtain fusion results rapidly,an image fusion algo-rithm based on Non-subsampled Shearlet Transform (NSST)combined with Compressed Sensing (CS) is presented.Firstly,the source images are decomposed with NSST;secondly,the high-frequency sub-band coefficients of the decomposed images are compressed,fused and reconstructed by CS;then, based on local area variance and local area energy,the low-frequency coefficients was fused;finally, the inverse NSST is used to get the final fused image.Because only the compressed values of the high frequency coefficients are fused,the image fusion effects can’t be affected,and the running time of the algorithm can be reduced.In this paper,the multi-focus image,medical image and infrared and visible images are used to verify the effectiveness of the algorithm.The simulation results indicate that this algorithm can achieve the fusion of the image without prior knowledge of the original image. When the image size is larger,although the fusion image quality is sacrificed,it can significantly im-prove the speed to reduce the time cost and hardware requirements.The algorithm provides an idea on how to satisfy the real time requirements in the fusion system,which has a great practical value.
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