针对多聚焦图像融合过程中,如何准确地检测聚焦区域以及克服检测过程中存在的配准错误和噪声敏感问题,提出一种结合滤波算子与双尺度分解的多聚焦图像融合算法。首先,对源图像进行高斯拉普拉斯滤波处理,并将获得的滤波图像与源图像作差分运算,分离出多源聚焦图像的高频信息;然后,利用基于结构的双尺度焦点度量方法对多源图像的边缘与局部高频信息进行分解处理,生成含有边缘互补信息的初始决策图;最后, 采用基于一致性检验方法对得到的初始决策图进行分步细化处理,生成融合决策图,并按照逐像素加权平均规则获得融合图像。实验结果表明,与其他聚焦策略相比,该聚焦区域检测方法对不同噪声具有更高的鲁棒性和更强的聚焦区域识别能力, 处理时间小于0.5 s。

Aiming at the problem of how to accurately detect the focusing region and how to overcome the registration error and noise sensitivity in the process of multi-focus image fusion, a multi-focus image fusion algorithm based on filter operator and double-scale decomposition is proposed. First, the algorithm performs Gaussian-Laplace filtering on the source image, and performs difference operation between the filtered image and the source image to separate out the high frequency information of the multi-source focused image. Then, an initial decision graph with complementary edge information is generated after decomposing the multi-source image edge and local high frequency information by the structure-based double scale focus measurement method. Finally, the initial decision graph is refined step by step based on the consistency test method to generate the fused decision graph, and the fused image is obtained according to the per-pixel weighted average rule. The experimental results show that, compared with other focusing strategies, this focusing region detection method has higher robustness and better recognition ability for different noises, and the processing time is less than 0.5 s.