摘要
多聚焦图像融合就是综合和处理多个源图像的信息,来获取对同一场景或物体的更为准确、更为全面、更为可靠的图像描述.对于可见光成像系统而言,特别是含有长焦距的光学镜头,对景深有一定的限制.因此,将场景中的所有物体都成像清晰是很难的,这个问题采用多聚焦图像融合技术可以很好解决.通过提出一种基于差异演化的多聚焦图像融合算法,首先将源图像进行分块,然后用清晰度评价函数比较对应图像块的清晰度,选择清晰度高的作为清晰图像块,最终重构融合图像.该方法的积极意义在于,优化后的图像块比没被优化的使用效果更佳.实验结果表明,从定量和视觉两方面评价,都明显优于遗传算法和其他传统的图像融合方法.
Image fusion means to synthesize multiple source images and get more accurate, more comprehensive and more reliable information about same scene or object. For visible light imaging system, optical lenses, particularly those with long focal lengths, suffer from the problem of limited depth of field. Consequently, it is often difficult to obtain good focus for all objects in the picture. But it can be solved by multi-focus image fusion. In this paper, a novel optimal method for multi-focus image fusion using differential evolution algorithm is presented. The source images are first decomposed into blocks. Then, the sharper blocks are selected by employing a sharpness criterion function. The selected blocks are finally combined to construct the fused image. The motivation of the proposed method lies in the fact that an optimized block size could be more effective than a fixed block size. Experimental results show that the proposed method outperforms genetic algorithm based method and other traditional methods in terms of both quantitative and visual evaluations.
出处
《合肥学院学报（自然科学版）》
2013年第1期29-32,共4页
Journal of Hefei University :Natural Sciences
基金
2011年度安徽理工大学青年教师科学研究基金项目资助
关键词
多聚焦图像融合
差异演化
遗传算法
Multi-focus image fusion
dfferential evolution
genetic algorithm
