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使用约束相机姿态子空间的快速非均匀去模糊

Fast Non-uniform Deblurring using Constrained Camera Pose Subspace
课程网址: http://videolectures.net/bmvc2012_hu_pose_subspace/  
主讲教师: Zhe Hu
开课单位: 加州大学
开课时间: 2012-10-09
课程语种: 英语
中文简介:
相机在曝光时的抖动通常会导致整个图像的不均匀模糊。最近的算法将非均匀模糊图像建模为离散位姿下摄像机观测到的图像的线性组合,并着重于估计每个位姿处的时间分数。虽然这些算法显示了有前途的结果,但它们仍然需要大量的计算负载。本文提出了一种新的单图像去模糊算法来去除非均匀模糊。我们估计了不同图像区域的局部模糊核,并利用反向投影得到了可能的相机姿势的初步猜测。通过在低维子空间中限制可能的相机姿态,我们迭代估计了相机姿态空间中每个姿态的权重。实验验证了该算法的有效性和有效性。
课程简介: Camera shake during exposure time often results in non-uniform blur across the entire image. Recent algorithms model the non-uniform blurry image as a linear combination of images observed by the camera at discretized poses, and focus on estimating the time fraction positioned at each pose. While these algorithms show promising results, they nevertheless entail heavy computational loads. In this work, we propose a novel single image deblurring algorithm to remove non-uniform blur. We estimate the local blur kernels at different image regions and obtain an initial guess of possible camera poses using backprojection. By restraining the possible camera poses in a low-dimensional subspace, we iteratively estimate the weight for each pose in the camera pose space. Experimental validations with the state-of-the-art methods demonstrate the efficiency and effectiveness of our algorithm for non-uniform deblurring.
关 键 词: 曝光时间; 相机抖动; 模糊算法
课程来源: 视频讲座网
最后编审: 2020-06-15:heyf
阅读次数: 53