核Fisher判别分析在多聚焦图像融合中的应用

提出一种基于核Fisher判别分析与图像块分割的多聚焦图像融合方法。该方法首先将源图像进行块分割，计算反映图像块聚焦程度的清晰度特征；再将源图像的部分区域作为训练样本，获得训练后的核Fisher判别分析参数；然后利用已知的核Fisher判别分析获得初步融合图像；最后对位于源图像清晰与模糊区域交界处的源图像块利用冗余小波变换进行处理后，得到最终融合图像。实验结果表明，该方法的图像融合效果优于常用图像融合方法，可在有效提高图像融合质量与减少计算量之间获得较好的折衷。

The application of kernel Fisher discriminant analysis in multifocus image fusion

A multifocus image fusion technique based on kernel Fisher discriminant analysis and image block segmentation is proposed. Firstly, the original images are decomposed into image blocks and focus measures of each image block are computed. To achieve the parameters of the trained kernel Fisher discriminant analysis, parts of the original images are chosen as the training exemplars. Then the initial fused image is acquired with the known kernel Fisher discriminant analysis. At last, the final merged image is obtained after the original image blocks, which are located near the border between the focused and blurred areas of the original images, through processing with the redundant wavelet transform. The experimental results show that the proposed method outperforms the conventional image fusion methods. A better tradeoff can be obtained between improving the image fusion quality and reducing the computational cost with the proposed method.