Multi-scale exposure fusion via gradient domain guided image filtering

F. Kou, Zhengguo Li, C. Wen, Weihai Chen
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引用次数: 83

Abstract

Multi-scale exposure fusion is an efficient way to fuse differently exposed low dynamic range (LDR) images of a high dynamic range (HDR) scene into a high quality LDR image directly. It can produce images with higher quality than single-scale exposure fusion, but has a risk of producing halo artifacts and cannot preserve details in brightest or darkest regions well in the fused image. In this paper, an edge-preserving smoothing pyramid is introduced for the multi-scale exposure fusion. Benefiting from the edge-preserving property of the filter used in the algorithm, the details in the brightest/darkest regions are preserved well and no halo artifacts are produced in the fused image. The experimental results prove that the proposed algorithm produces better fused images than the state-of-the-art algorithms both qualitatively and quantitatively.
基于梯度域引导图像滤波的多尺度曝光融合
多尺度曝光融合是一种将高动态范围场景中不同曝光的低动态范围(LDR)图像直接融合成高质量LDR图像的有效方法。它可以产生比单尺度曝光融合更高质量的图像,但有产生晕晕伪影的风险,并且不能很好地保留融合图像中最亮或最暗区域的细节。在多尺度曝光融合中引入了一种保持边缘的平滑金字塔。该算法利用滤波器的边缘保持特性,很好地保留了最亮/最暗区域的细节,融合后的图像不会产生晕影。实验结果表明,该算法在质量和数量上都优于现有的融合算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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