Aim. The introduction of the full paper reviews a number of papers in the open literature and then proposes what we believe to be a new and relatively more effective algorithm, which is explained in sections 1 and 2.The core of section 2 consists of: (1) we use wavelet transforms to perform the multi-scale decomposition of source images, thus obtaining their low-frequency and high-frequency sub-images respectively; (2) we apply the neighborhood normalized gradients of pixels to fusing the low-frequency sub-images so as to obtain their low-frequency fusion coefficients and apply the neighborhood variances of pixels to fusing high-frequency sub-images so as to obtain their high-frequency fusion coefficients; (3) we use the inverse wavelet transforms to perform the wavelet reconstruction of the fused sub-images, as shown by the block diagram in Fig. 1, thus ohtaining their fused image. Section 3 did two things to compare the fusion effects of different image fusion algorithms : ( 1 ) we did expenments on the fusion of two grey images and two color images respectively; the experimental results, given in Figs. 2 and 3,and their analysis show preliminarily that our multi-focus image fusion algorithm can effectively improve the quality of fused image in terms of its definition and contrast; (2) we performed the objective evaluation of the performance of our image fusion algorithm; the evaluation results, given in Table 1, and their analysis also show preliminarily that our image fusion algorithm can reduce the numher of decomposition layers and the computation load, thus being superior to other algorithms in terms of root mean squares error, entropy and peak signal to noise ratio.%提出了一种基于小波变换和邻域特征的多聚焦图像融合算法.该算法首先采用小波变换对源图像进行多尺度分解,得到低频和高频子图像;然后对低频子图像采用基于邻域归一化梯度的方法得到低频融合系数,对高频子图像采用基于邻域方差的方法得到高频融合系数;最后进行小波重构得到融合图像.采用均方根误差、信息熵以及峰值信噪比等评价标准,将该算法与传统融合方法的融合效果进行了比较.实验结果表明,该算法所得融合图像的效果和质量均有明显提高.
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