any computational photography applications require sets of images that are captured simultaneously from the same viewpoint but have different image sensors and imaging parameters. In this article, we address the problem of designing efficient multisensor cameras that can capture such images. Although for two-and three-sensor cameras ad-hoc designs are often effective, the design problem becomes challenging as the number of sensors increases. We demonstrate results on cameras created using our new design paradigm that contains as many as eight sensors. (To gain a better sense of the issues we're tackling in this article, read the "Basic Background and Considerations" sidebar.) In this article, we consider the design of monocular multiview optical systems that form optical splitting trees, where the optical path topology takes the shape of a tree because of recursive beam splitting. Designing optical splitting trees is challenging when it requires many views with specific spectral properties. We introduce a manual design paradigm for optical splitting trees and a computer-assisted design tool to create efficient splitting-tree cameras. The tool accepts as input a specification for each view and a set of weights describing the user's relative affinity for efficiency, measurement accuracy, and economy. An optimizer then searches for a design that maximizes these weighted priorities. Our tool's output is a splitting-tree design that implements the input specification and an analysis of the efficiency of each root-to-leaf path. Automatically designed trees appear comparable to those designed by hand; we even show some cases where they're superior. We've previously reviewed and demonstrated capturing multiple monocular views in various contexts, but rarely have more than three simultaneous views been captured. (To see others' approaches to this and similar problems, see the "Related Work" sidebar on page 40.) Our approach makes design, implementation, and calibration practical for many more sensors. To demonstrate the practical application of our splitting tree paradigm and optimizer, we built the configurable optical splitting tree system shown in Figure 1 (on page 34) that captures up to eight views. Assisted by our design tool, we were able to use this single device to implement several different popular computational videography applications: high-dynamic range (HDR), multifocus, high-speed, and hybrid high-speed multispectral video. In this article, we demonstrate results for each of these. Optical splitting trees are schematic representations of tree-topology filter systems. The edges are light paths and the nodes are optical elements. Nodes with a single child represent filters and lenses. Nodes with multiple children are beam splitters. Leaf nodes are sensors. The plenoptic field enters the system at the root. The physical path length to each sensor's optical center is identical. However, a sensor's tree depth (the number of internal nodes between it and the root) might differ. Figure 2 shows a schematic of a full binary splitting tree, viewed from above, where light enters on the upper right. This layout packs components into a small form factor with no optical path occluded. The thick black lines are light baffles preventing the reflective child of each splitter from imaging stray light. Further abstracting the structure and using the symbols in Figure 3 , we can emphasize the most significant elements. In each case in Figure 3 , light enters at the top and emerges at the bottom. Spectral filters are narrow bandpass filters centered at the specified value. "Sensor + Lens" sets specify imaging properties. The neutral density (ND) filter reduces the amount of transmitted light, equally across all wavelengths. The plate mirror splits incoming light into two directions. Unless specified, it reflects 50 percent and transmits 50 percent of The authors present a design framework and optimizer for computational systems containing many sensors on a common optical axis.
A hardware-parallel solution is to use optical splitting trees REF .
Optical Splitting Trees for High-Precision Monocular Imaging
IEEE Computer Graphics and Applications
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