结合小波变换和自适应分块的多聚焦图像快速融合  

Multi-focus image fusion based on wavelet transform and adaptive block

作　　者：刘羽[1] 汪增福[1,2] 

机构地区：[1]中国科学技术大学自动化系,合肥230027 [2]中国科学院合肥智能机械研究所,合肥230031

出　　处：《中国图象图形学报》2013年第11期1435-1444,共10页Journal of Image and Graphics

基　　金：国家自然科学基金面上项目(60875026);中央高校基本科研业务费(WK2100100020);安徽省科技专项(11010202192)

摘　　要：提出一种基于小波变换和自适应分块相结合的多聚焦图像快速融合算法。该算法以小波变换为框架,对小波低频系数采用自适应尺寸分块的方法进行融合,图像块的尺寸由差分进化算法优化求解,然后对此低频融合结果进行精细化处理,得到一幅能精确到每个系数来源的标签图,再利用局部小波能量与该标签图相结合的方法对小波高频系数进行融合,最后重构得到融合结果。实验结果表明,该算法的融合结果在主观视觉效果和客观评价准则两方面均可以接近甚至达到图像融合领域的最好水平,且在提高融合质量和降低运算代价间取得了较好的折衷。A new fast fusion algorithm for multi-focus images based on wavelet transform and adaptive block is proposed in this paper. The proposed algorithm is implemented under the framework of wavelet transformation. For the low frequency coefficients, an adaptive block-based fusion technique is applied, where the optimal block size can be calculated by using differential evolution algorithm. Moreover, a pixel-level label map, which can accurately indicate the origin information of each pixel, is obtained by refining the above low-frequency fusion-result. On the other hand, the high frequency fusion task is completed by combining the local wavelet energy based rule with the information offered by the label map. Finally, the fused image is obtained by performing the inverse wavelet transform. Experimental results demonstrate that the performance of the proposed method is comparable with the state-of-the-art methods on both subjective visual perception and objective evaluation criteria. Furthermore, the proposed algorithm can achieve a good balance between improving the fusion quality and reducing the computational cost.

关 键 词：多聚焦图像融合 小波变换 差分进化算法 聚焦程度测量 

分 类 号：TP911.73[自动化与计算机技术]