Image Transformations and Blurring
Image Transformations and Blurring
复制标题
图像变换和模糊
DOI：
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复制
发表时间：
2009
影响因子：
23.6
通讯作者：
Y. Aloimonos
中科院分区：
文献类型：
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作者：
Justin Domke;Y. Aloimonos
由于相机在测量过程中会使入射光模糊，因此同一表面的不同图像不包含有关该表面的相同信息。因此，一般来说，场景的多个视图中的对应点具有不同的图像强度。虽然多视图几何结构约束对应点的位置，但它不给出对应位置处的信号之间的关系。本文对这些关系作了初步探讨。我们首先开发的概念“idealrdquo和“realrdquo图像，分别对应于原始的入射光和测量信号。该框架将成像的滤波和几何方面分开。然后，我们考虑如何从另一个表面的一个视图合成;如果两个视图之间的变换是仿射的，那么当且仅当仿射矩阵的奇异值为正时，这是可能的。接下来，我们考虑如何将表面的多个视图中的信息联合收割机组合成单个输出图像。通过开发一种称为“频率分割”的新工具，我们展示了如何在不知道模糊内核的情况下实现这一点。
Since cameras blur the incoming light during measurement, different images of the same surface do not contain the same information about that surface. Thus, in general, corresponding points in multiple views of a scene have different image intensities. While multiple-view geometry constrains the locations of corresponding points, it does not give relationships between the signals at corresponding locations. This paper offers an elementary treatment of these relationships. We first develop the notion of "idealrdquo and "realrdquo images, corresponding to, respectively, the raw incoming light and the measured signal. This framework separates the filtering and geometric aspects of imaging. We then consider how to synthesize one view of a surface from another; if the transformation between the two views is affine, it emerges that this is possible if and only if the singular values of the affine matrix are positive. Next, we consider how to combine the information in several views of a surface into a single output image. By developing a new tool called "frequency segmentation," we show how this can be done despite not knowing the blurring kernel.