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Multi-scale exposure fusion via gradient domain guided image filtering

机译:通过梯度域引导图像滤波进行多尺度曝光融合

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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.
机译:多尺度曝光融合是一种将高动态范围(HDR)场景的不同曝光的低动态范围(LDR)图像直接融合为高质量LDR图像的有效方法。它可以产生比单倍曝光融合更高质量的图像,但是有产生光晕伪像的风险,并且不能在融合图像中很好地保留最亮或最暗区域中的细节。在本文中,为多尺度曝光融合引入了一个保留边缘的平滑金字塔。受益于算法中使用的滤镜的边缘保留特性,可以很好地保留最亮/最暗区域中的细节,并且在融合图像中不会产生任何光晕伪像。实验结果证明,与现有技术相比,该算法在定性和定量方面都能产生更好的融合图像。

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