【SIGGRAPH Asia 2012专题*技术论文】颜色和照片：基于强大的补丁的动态场景的HDR重建

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【SIGGRAPH Asia 2012专题*技术论文】
颜色和照片：基于强大的补丁的动态场景的HDR重建Robust Patch-Based HDR Recons***ction of Dynamic Scenes
Pradeep Sen, Nima Khademi Kalantari, Maziar Yaesoubi, Soheil Darabi, Dan B Goldman, Eli Shechtman 我们的算法需要输入一个动态场景的曝光（未预先对齐）括号内的一组连续的，并输出一个高品质的HDR结果随着一个重构组对准的图像在每个曝光。在左边是三个7低动态范围（LDR）采取的一个标准的，手持式的数码相机，具有主体和照相机运动的输入源。这些都是其次我们的算法的输出在每个曝光，未示出的参考图像对齐。不仅我们的算法正确对齐的图像，尽管复杂的运动，但它也保持着微妙的灯光在每次曝光的细节（例如，在圣诞树的帽子，衬衫上的阴影，细节的亮点），这将有助于信息的最后的光芒地图。在右边的是我们的tonemapped HDR result.For7输入图像一组这样的，我们的算法需要不到3分钟来计算最终的结果，在1350×900的分辨率。 Our algorithm takes as input a sequential set of bracketed exposures of a dynamic scene (not pre-aligned) and outputs a high-quality HDR result along with a recons***cted set of aligned images at each exposure. On the left are three of seven low-dynamic range(LDR) input sources taken with a standard, hand-held digital camera that have both subject and camera motion. These are followed by the outputs of our algorithm at each exposure, aligned to the reference image which is not shown. Not only does our algorithm properly align the images despite the complex motion, but it also maintains the subtle lighting detail in each exposure (e.g., highlights on the hat, shading on the shirt, detail in the Christmas tree) that will contribute information to the final radiance map. On the right is our tonemapped HDR result.For a set like this with 7 input images, our algorithm takes less than 3 minutes to compute the final result at 1350 × 900 resolution.
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