IEEE Workshop on Applications of Computer Vision (WACV'11)
All-in-focus imaging is a computational photography technique that produces images free of defocus blur by capturing a stack of images focused at different distances and merging them into a single sharp result. Current approaches assume that images have been captured offline, and that a reasonably powerful computer is available to process them. In contrast, we focus on the problem of how to capture such input stacks in an efficient and scene-adaptive fashion. Inspired by passive autofocus techniques, which select a single best plane of focus in the scene, we propose a method to automatically select a minimal set of images, focused at different depths, such that all objects in a given scene are in focus in at least one image. We aim to minimize both the amount of time spent metering the scene and capturing the images, and the total amount of high-resolution data that is captured. The algorithm first analyzes a set of low-resolution sharpness measurements of the scene while continuously varying the focus distance of the lens. From these measurements, we estimate the final lens positions required to capture all objects in the scene in acceptable focus. We demonstrate the use of our technique in a mobile computational photography scenario, where it is essential to minimize image capture time (as the camera is typically handheld) and processing time (as the computation and energy resources are limited).
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Daniel Vaquero, Natasha Gelfand, Marius Tico, Kari Pulli, and Matthew Turk. Generalized Autofocus. In IEEE Workshop on Applications of Computer Vision (WACV'11), Kona, Hawaii, January 2011.