Multi-exposure image fusion is an effective method for depicting high dynamic range of the target scene in a single image. However, there are still some problems remaining: the preserving of global contrast, the preserving of the local details in saturated regions, and the existence of halo artifacts. To solve these problems, this paper proposes a new multi-exposure image fusion algorithm with detail enhancement. Firstly, the well-exposedness evaluation function, the chromatic information evaluation function and the local detail preserved function are used to measure weight maps. Then, an improved multi-exposure fusion framework based on pyramid decomposition is proposed to further enhance the details. The experimental results demonstrate that the proposed algorithm can preserve more details than the state-of-the-art. In the view of appreciation, our approach could produce a more realistic brightness distribution of target scene as well as avoid halo artifacts.

中文翻译：

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一种基于细节增强的多曝光图像融合改进算法

多曝光图像融合是一种在单幅图像中描绘目标场景高动态范围的有效方法。然而，仍然存在一些问题：全局对比度的保留、饱和区域局部细节的保留以及光晕伪影的存在。针对这些问题，本文提出了一种新的具有细节增强的多曝光图像融合算法。首先，利用良好曝光度评价函数、色度信息评价函数和局部细节保留函数测量权重图。然后，提出了一种改进的基于金字塔分解的多曝光融合框架，以进一步增强细节。实验结果表明，所提出的算法可以保留比最先进的算法更多的细节。在欣赏方面，