Projection defocus analysis for scene capture and image display
Projection defocus analysis for scene capture and image display
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DOI：
10.1145/1179352.1141974
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发表时间：
2006-07
期刊：
影响因子：
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通讯作者：
Li Zhang;S. Nayar
中科院分区：
文献类型：
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作者：
Li Zhang;S. Nayar
为了产生明亮的图像，投影仪的光圈较大，因此景深较窄。在本文中，我们提出了基于投影离焦分析的鲁棒场景捕获和增强图像显示方法。我们用线性系统来模拟投影仪的散焦。利用该模型提出了一种新颖的时间离焦分析方法，通过估计其投影离焦峰的频域参数来恢复每个相机像素处的深度。与大多数深度恢复方法相比，我们的方法在深度不连续附近更准确。此外，通过使用同轴投影仪-摄像机系统，我们确保在所有摄像机像素上计算深度，没有任何缺失的部分。我们表明，恢复的场景几何可以用于重新聚焦合成和基于深度的图像合成。使用相同的投影仪离焦模型和估计技术，我们还提出了一种离焦补偿方法，该方法以空间变化，深度依赖的方式过滤投影图像，以最小化投影到场景后的离焦模糊。这种方法在不改变投影仪光学器件的情况下有效地增加了投影仪的景深。最后，我们提出了一种利用投影仪离焦的算法，以减少数字投影仪产生的强像素伪影，同时保持投影图像的质量。我们已经用真实场景对我们的每一种方法进行了实验验证。
In order to produce bright images, projectors have large apertures and hence narrow depths of field. In this paper, we present methods for robust scene capture and enhanced image display based on projection defocus analysis. We model a projector's defocus using a linear system. This model is used to develop a novel temporal defocus analysis method to recover depth at each camera pixel by estimating the parameters of its projection defocus kemel in frequency domain. Compared to most depth recovery methods, our approach is more accurate near depth discontinuities. Furthermore, by using a coaxial projector-camera system, we ensure that depth is computed at all camera pixels, without any missing parts. We show that the recovered scene geometry can be used for refocus synthesis and for depth-based image composition. Using the same projector defocus model and estimation technique, we also propose a defocus compensation method that filters a projection image in a spatially-varying, depth-dependent manner to minimize its defocus blur after it is projected onto the scene. This method effectively increases the depth of field of a projector without modifying its optics. Finally, we present an algorithm that exploits projector defocus to reduce the strong pixelation artifacts produced by digital projectors, while preserving the quality of the projected image. We have experimentally verified each of our methods using real scenes.