【摘要】 针对现有多聚焦图像融合算法存在的伪影和信息残留问题，提出了一种依据图像聚焦特性，最大程度保留各区域信息和清晰度的算法。首先，通过区域检测得到聚焦区域决策图，利用该决策图进行初始融合和边界提取，得到边界区域决策图；其次，利用ACS网络学习多聚焦图像的融合规则，生成网络融合图；最后，根据边界区域决策图对初始融合图和网络融合图进行加权求和，得到最终的融合图像。实验结果表明：该算法在聚焦区域和边界区域都优于其它比较算法，各项评估指标分别提高4.8%和1.5%以上；同时主观效果更符合HVS。实验证明了在保留源图像的细节信息和避免各个区域的视觉伪影上，该算法都能取得很好的效果。更多还原

【Abstract】 To address the problem of artifacts and information residuals in the existing multi-focus image fusion algorithms, an algorithm is proposed to maximize the retention of information and clarity of each region based on the focusing characteristics of the image. Firstly, the focus region decision map is obtained by region detection, which is then used for initial fusion and boundary extraction to obtain the boundary region decision map; secondly, the ACS Network is used to learn the fusion rules of multi-focus images and generate the network fusion image; finally, the initial fusion image and the network fusion image are weighted and summed according to the boundary region decision map to obtain the final fusion image. The experimental results demonstrate that the algorithm outperforms other comparable algorithms in both the focus region and the boundary region, and the evaluation indexes are improved by more than 4.8% and 1.5%, respectively; Meanwhile, the subjective effect is more in line with HVS. The experiments have proved that the algorithm achieves good results in retaining the detailed information of the source image and avoiding visual artifacts in various regions.更多还原