CAJ | 학술논문

基于人类视觉系统及信号的过完备稀疏表示理论，提出了一种基于多尺度字典的红外与微光图像融合方法。首先把输入的红外与微光图像按照高斯金字塔模型分解，用 DCT 字典作为初始字典按照四叉树的结构进行分解，对于各尺度的字典按照 K-SVD 算法独立训练更新，构造出多尺度学习字典。其次在该字典下利用改进的OMP算法得到输入源图像各自的稀疏系数，然后按照最优化融合图像与输入源图像的欧氏距离、融合图像方差的准则，建立一个融合图像稀疏系数的最优化函数，最后通过求解该函数的l1范数得到融合图像。实验结果表明：该算法的融合效果优于小波变换法、Laplacian塔型方法以及PCA方法等传统融合方法。
기우인류시각계통급신호적과완비희소표시이론，제출료일충기우다척도자전적홍외여미광도상융합방법。수선파수입적홍외여미광도상안조고사금자탑모형분해，용 DCT 자전작위초시자전안조사차수적결구진행분해，대우각척도적자전안조 K-SVD 산법독립훈련경신，구조출다척도학습자전。기차재해자전하이용개진적OMP산법득도수입원도상각자적희소계수，연후안조최우화융합도상여수입원도상적구씨거리、융합도상방차적준칙，건립일개융합도상희소계수적최우화함수，최후통과구해해함수적l1범수득도융합도상。실험결과표명：해산법적융합효과우우소파변환법、Laplacian탑형방법이급PCA방법등전통융합방법。
A novel infrared and low light level image fusion algorithm based on multi-scale sparse representation is introduced on the basis of the Human Visual System and over-complete sparse representation theory in this paper. Firstly, infrared and low light level images are decomposed according to the Gaussian pyramid model. Then a multi-scale learned dictionary is obtained by using an efficient quadtree decomposition of the DCT dictionary which is considered as the initial dictionary and each scale dictionary independent training update using K-SVD algorithm. We use the improved OMP algorithm with the dictionary to get the input sparse coefficients of source images. And then we get an optimization function of the fusion image sparse coefficients by optimizing the Euclidean distances between fused image and each input, weighted by their own variance. Finally, we obtain the fusion image by solving the l1 norm of the function. The experimental results show that the proposed method exhibits considerably higher fusion performance than the typical methods such as the wavelet transform method, the Laplacian pyramid method and Principal Component Analysis method.