图像差与加权核范数最小化的压缩图像融合

现有的图像融合算法存在非线性操作产生的噪声干扰和空间复杂度高等问题，使得融合图像易失真和丢失信息。一些学者提出的压缩感知图像融合算法能有效改善这一问题，但大多忽略了图像矩阵的低秩性，往往会降低融合质量。由此，将压缩感知融合技术与低秩矩阵逼近方法相结合，提出基于信息论图像差与自适应加权核范数最小化的图像融合算法。该算法由3个阶段组成。首先，将2幅源图像通过小波稀疏基稀疏化，并利用结构随机矩阵压缩采样，得到测量输出矩阵。然后，将测量输出矩阵进行分块，再利用图像差融合算法得到融合后的测量输出矩阵块。最后，利用自适应加权核范数最小化优化得到的块权重，通过正交匹配追踪法重建融合图像。实验结果表明了该算法的有效性和普适性，并且在多种评价指标上优于其他融合算法。

Compressed image fusion based on imagedifference and weighted kernel norm minimization

Existing image fusion algorithms have some problems caused by non-linear operations, such as noise interference and spatial complexity, which make fused images easy to cause distortion and information loss. Compressed sensing image fusion algorithms proposed by some scholars can effectively improve this problem. However, most of them neglect the low rank of image matrix, thus often reducing the quality of fusion. Thus, combining the compressed sensing fusion technology with the low rank matrix approximation method, we propose an image fusion method based on information theory image difference and adaptive weighted kernel norm minimization. The method consists of three stages. Firstly, the two source images are sparsed by wavelet sparse basis, and the measurement output matrix is obtained by compressing the samplings with structural random matrix. Then, the measurement output matrix is divided into blocks, and the fused measurement output matrix blocks are obtained by using the image difference fusion algorithm. Finally, the block weights obtained by adaptive weighted kernel norm minimization method are used to reconstruct the fused image by the orthogonal matching pursuit method. Experimental results verify the validity and universality and show that our method is superior to other fusion algorithms in many evaluation indexes.