基于全局和局部特征的Patch多曝光图像融合

Ji-hee Kim, Hyunho Choi, Jechang Jeong
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引用次数: 1

摘要

本文提出了一种改进由信号强度、信号结构和平均强度组成的权重映射部分的算法。传统的基于patch的权重图会使图像的亮度向一侧偏移,造成图像信息的丢失和意想不到的伪影,造成图像亮度的整体不平衡。在本研究中,我们提出了一种改进权重图的新算法。首先,使用最大值的顺序统计过滤器。其次,利用拉普拉斯算子对不锐利的掩蔽滤波器进行滤波。第三,利用变换进行线性组合。该算法通过降低图像的过饱和度来防止图像信息的丢失,通过增加对比度来准确表示暗区和亮区,并保留边缘等细节。通过主观和客观的实验结果,证实了该算法比传统算法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Exposure Image Fusion Based on Patch using Global and Local Characteristics
In this paper, we propose an algorithm that improves the weight map part consisting of signal strength, signal structure, and mean intensity. The patch-based conventional weight map causes the brightness of the image to be shifted to one side, resulting in loss of image information, unexpected artifacts, and an overall unbalance in image brightness. In this study, we propose a novel algorithm by improving the weight map. First, the order-statistic filter using maximum values. Second, the unsharp masking filter using Laplacian. Third, the linear combination using gamma transformation. The proposed algorithm prevents the loss of image information by reducing the over-saturation of the image, accurate representation of dark and bright areas by increasing contrast, and preserve the detail such as the edge. Through subjective and objective experimental results, it is confirmed that the proposed algorithm shows better performance than the conventional algorithms.
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