Robust High Dynamic Range Imaging by Rank Minimization
Robust High Dynamic Range Imaging by Rank Minimization
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DOI：
10.1109/tpami.2014.2361338
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发表时间：
2015-06-01
影响因子：
23.6
通讯作者：
Kweon, In So
中科院分区：
文献类型：
--
作者：
Oh, Tae-Hyun;Lee, Joon-Young;Kweon, In So
本文介绍了一种利用秩最小化的新型高动态范围（HDR）成像算法。假设相机对场景辐射呈线性响应，用不同曝光时间拍摄的输入低动态范围（LDR）图像呈现线性相关性，并且当将每个对应像素的强度堆叠在一起时形成一个秩为1的矩阵。在实际应用中，相机运动导致的错位、运动物体的存在、饱和度以及图像噪声破坏了LDR图像的秩 - 1结构。为了解决这些问题，我们提出了一种秩最小化算法，该算法可同时对齐LDR图像并检测异常值以实现稳健的HDR生成。我们使用合成示例系统地评估了我们算法的性能，并使用具有挑战性的真实世界示例将我们的结果与最先进的HDR算法的结果进行了定性比较。
This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes rank minimization. Assuming a camera responses linearly to scene radiance, the input low dynamic range (LDR) images captured with different exposure time exhibit a linear dependency and form a rank-1 matrix when stacking intensity of each corresponding pixel together. In practice, misalignments caused by camera motion, presences of moving objects, saturations and image noise break the rank-1 structure of the LDR images. To address these problems, we present a rank minimization algorithm which simultaneously aligns LDR images and detects outliers for robust HDR generation. We evaluate the performances of our algorithm systematically using synthetic examples and qualitatively compare our results with results from the state-of-the-art HDR algorithms using challenging real world examples.