Multi-exposure fusion is a technique used to generate a high-dynamic-range image without calculating the camera response function and without compressing its ranges with the tone mapping process. There are many schemes for fusing multi-exposure images. One of the famous schemes is the pyramid-based blending, which fuses multi-exposure images together based on the concept of multi-resolution blending. The computational time of this method is fast, and the resulting image is satisfying. However, in some cases, the pyramid-based blending method has a trade-off between preserving local areas and the quality in terms of smoothness boundaries. To solve this trade-off, we propose a new pyramid-based blending scheme, called the two-layer pyramid-based blending method for multi-exposure fusion. We found that, for some sets of input images, we have to generate a virtual photograph and add it into those sets. Thus, we first propose a criterion for adding the virtual photograph. Then, we construct a concatenation of two pyramid-based blending methods, in which the resulting images from different levels of the first-layer pyramid-based blending method are used as inputs of the second-layer one. The test results showed that the resulting images were satisfying, and their objective evaluation scores regarding the perceptual image quality and detail-preserving assessment were high, even though they are not highest, compared with those of methods used in our experiments. Also, there is one advantage of the proposed method compared with the others. That is, the proposed method could preserve details in some areas, whereas the other methods could not.

多曝光融合是一种用于生成高动态范围图像的技术，无需计算相机响应函数，也无需通过色调映射过程压缩其范围。有许多用于融合多重曝光图像的方案。一种著名的方案是基于金字塔的混合，它基于多分辨率混合的概念将多曝光图像融合在一起。该方法的计算时间很快，并且得到的图像令人满意。但是，在某些情况下，基于金字塔的混合方法在保持局部区域和质量（就平滑度边界）方面进行了权衡。为解决此问题，我们提出了一种新的基于金字塔的混合方案，称为用于多曝光融合的两层基于金字塔的混合方法。我们发现，对于某些输入图像集，我们必须生成虚拟照片并将其添加到这些集合中。因此，我们首先提出添加虚拟照片的标准。然后，我们构造了两种基于金字塔的混合方法的串联，其中将来自第一层基于金字塔的混合方法的不同级别的结果图像用作第二层基于金字塔的混合方法的输入。测试结果表明，得到的图像令人满意，并且与我们实验中使用的方法相比，它们在感知图像质量和细节保留评估方面的客观评价得分很高，即使不是最高。此外，与其他方法相比，该方法还有一个优点。也就是说，所提出的方法可以保留某些区域的细节，而其他方法则不能。