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Although there has been much interest in computational photography within the research and photography communities, progress has been hampered by the lack of a portable, programmable camera with sufficient image quality and computing power. To address this problem, we have designed and implemented an open architecture and API for such cameras: the(More)
In this paper we investigate the problem of recovering the motion blur point spread function (PSF) by fusing the information available in two differently exposed image frames of the same scene. The proposed method exploits the difference between the degradations which affect the two images due to their different exposure times. One of the images is mainly(More)
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,(More)
In this paper we introduce a new method of motion blur identification that relies on the availability of two, differently exposed, image shots of the same scene. The proposed approach exploits the difference in the degradation models of the two images in order to identify the point spread function (PSF) corresponding to the motion blur, that may affect the(More)
The objective of image stabilization is to prevent or remove the motion blur degradation from images. We introduce a new approach to image stabilization based on combining information available in two differently exposed images of the same scene. In addition to the image normally captured by the system, with an exposure time determined by the illumination(More)