Abstract: Focusing on the problems of low quality of obtained images and low algorithm efficiency of existing multi-exposure image fusion algorithms, a multi-exposure image fusion algorithm based on local features of scene was proposed. Firstly, the image sequence with different exposures was divided into regular patches with overlapping regions of some pixels between neighbouring patches. For static scenes, the weight for each patch was calculated based on local variance, local visibility and local saliency; for dynamic scenes, in addition to the three features described above, local similarity was also used to remove ghost caused by moving objects. Then, the optimal patches were obtained based on the weighted sum method. Finally, the output patches were fused together and the pixels in overlapping regions were averaged to obtain the final fusion result. With 12 sets of exposure sequences of different natural scenes, the proposed algorithm was compared with 7 existing pixel-based and feature-based algorithms in subjective and objective aspects. Experimental results demonstrate that the proposed algorithm preserves more details and obtains good visual effects in both static scenes and dynamic scenes. At the same time, the proposed algorithm also maintains high computational efficiency.

Key words: multi-exposure image fusion, local variance, local visibility, local similarity, ghost

摘要： 针对现有多曝光图像融合算法得到的图像质量不高以及算法效率低的问题，提出了基于场景局部特征的多曝光图像融合算法。首先，将不同曝光量的图像序列划分为规则的图像块，并且相邻的图像块有一定像素的重叠区域。对于静态场景，根据图像的局部方差、局部可视性以及局部显著性特征这三个指标计算每一个图像块的权重值；对于动态场景，除了应用前面所述的三个局部特征指标外，还需要将局部相似性指标用于动态场景融合过程中以去除运动物体导致的鬼影现象。其次，利用加权求和的方法得到最佳的图像块。最后，将输出的图像块进行融合，并且将图像块重叠区域的像素求平均，从而得到最终的融合结果。选取12组不同自然场景的曝光序列，从主观和客观两方面与现有的基于像素和基于特征的7种算法进行了分析和比较。实验结果表明：无论在静态场景还是动态场景的测试中，所提算法都保留了更多的场景信息，获得了令人满意的视觉效果，同时该算法还保持了较高的计算效率。

关键词: 多曝光图像融合, 局部方差, 局部可视性, 局部相似性, 鬼影