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首页> 外文期刊>Journal of visual communication & image representation >Gradient field multi-exposure images fusion for high dynamic range image visualization
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Gradient field multi-exposure images fusion for high dynamic range image visualization

机译:梯度场多曝光图像融合,实现高动态范围图像可视化

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摘要

This paper presents a novel method for fusing multi-exposure images into a low dynamic range (LDR) image that is suitable for display and visualization but it contains details in the high dynamic range (HDR) counterpart. Fused gradient field is derived from the structure tensor of inputs based on multidimensional Riemannian geometry with a Euclidean metric assumed. Afterwards, a new method is proposed for modifying the gradient field iteratively with twice average filtering and nonlinearly compressing in multi-scales. These modification operations are all done at the finest resolution. The result is obtained through solving a Poisson equation then linearly stretching to the common range. Experimental results demonstrate the efficiency and effectiveness of this method.
机译:本文提出了一种用于将多曝光图像融合到低动态范围(LDR)图像中的新方法,该方法适用于显示和可视化,但其中包含高动态范围(HDR)副本中的详细信息。融合的梯度场是从输入的结构张量导出的,该输入张量基于多维的黎曼几何并假定了欧几里德度量。然后,提出了一种新的方法,该方法通过两次平均滤波和多尺度非线性压缩来迭代地修改梯度场。这些修改操作都是在最高分辨率下完成的。通过求解泊松方程然后线性拉伸到公共范围可以得到结果。实验结果证明了该方法的有效性。

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