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首页> 外文期刊>Journal of mathematical imaging and vision >Smart depth of field optimization applied to a robotised view camera
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Smart depth of field optimization applied to a robotised view camera

机译：智能景深优化应用于自动取景器

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摘要

The great flexibility of a view camera allows the acquisition of high quality images that would not be possible any other way. Bringing a given object into focus is however a long and tedious task, although the underlying optical laws are known. A fundamental parameter is the aperture of the lens entrance pupil because it directly affects the depth of field. The smaller the aperture, the larger the depth of field. However a too small aperture destroys the sharpness of the image because of diffraction on the pupil edges. Hence, the desired optimal configuration of the camera is such that the object is in focus with the greatest possible lens aperture. In this paper, we show that when the object is a convex polyhedron, an elegant solution to this problem can be found. It takes the form of a constrained optimization problem, for which theoretical and numerical results are given. The optimization algorithm has been implemented on the prototype of a robotised view camera.
机译：观察相机的极大灵活性允许获取高质量图像，而这是其他任何方式都不可能实现的。尽管已知基本的光学定律，但是将给定对象聚焦是一项艰巨而繁琐的任务。基本参数是镜片入射光瞳的光圈，因为它直接影响景深。光圈越小，景深越大。但是，过小的光圈会由于光瞳边缘上的衍射而破坏图像的清晰度。因此，照相机的期望的最佳配置是使得物体以最大可能的镜头光圈对准焦点。在本文中，我们表明，当对象是凸多面体时，可以找到该问题的理想解决方案。它采取约束优化问题的形式，给出了理论和数值结果。该优化算法已在机器人观察相机的原型上实现。

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