The most important part of the common algorithms for multi-exposure image fusion (MEF) is the selection of features and metrics that are appropriate for weight map extraction. This study presents a structure-based multi-exposure image fusion by employing the phase congruency (PC) of the input image. The main idea behind PC-based analysis is that the locations of image key attributes are at points where frequency components are maximally in phase. PC detects the details of an image invariant to its contrast and also emphasises on the texture- or structure-based features. In this work, alongside intensity-based maps, the extracted PC-based map is utilised for MEF in a pyramidal manner. Several experiments conducted on the benchmark dataset including a variety of natural multi-exposed image sequences to evaluate the proposed algorithm. Quantitative evaluations in terms of MEF structural similarity index and visual quality assessments show that the proposed method achieves better performance and produces comparable fused images in comparison to other approaches.

中文翻译：

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通过输入图像的相位一致性与基于强度的贴图的金字塔积分进行多曝光图像融合

多曝光图像融合（MEF）通用算法中最重要的部分是选择适合权重图提取的特征和度量。这项研究通过利用输入图像的相位一致性（PC）提出了一种基于结构的多重曝光图像融合。基于PC的分析背后的主要思想是，图像关键属性的位置位于频率分量最大同相的点。PC可以检测出对比度不变的图像细节，并强调基于纹理或结构的特征。在这项工作中，与基于强度的地图一起，将提取的基于PC的地图以金字塔的方式用于MEF。在基准数据集上进行了一些实验，包括各种自然的多重曝光图像序列，以评估所提出的算法。