Zhou Changyin author Columbia University. Computer Science Nayar Shree K. author Columbia University. Computer Science Columbia University. Computer Science originator contributor text Technical reports New York Department of Computer Science, Columbia University 2012 For a given camera setting, scene points that lie outside of depth of field (DOF) will appear defocused (or blurred). Defocus causes the loss of image details. To recover scene details from a defocused region, deblurring techniques must be employed. It is well known that the deblurring quality is closely related to the defocus kernel or point-spread-function (PSF), whose shape is largely determined by the aperture pattern of the camera. In this paper, we propose a comprehensive framework of aperture evaluation for the purpose of defocus deblurring, which takes the effects of image noise, deblurring algorithm, and the structure of natural images into account. By using the derived evaluation criterion, we are able to solve for the optimal coded aperture patterns. Extensive simulations and experiments are then conducted to compare the optimized coded apertures with previously proposed ones. The proposed framework of aperture evaluation is further extended to evaluate and optimize extended depth of field (EDOF) cameras. EDOF cameras (e.g., focal sweep and wavefront coding camera) are designed to produce PSFs which are less sensitive to depth variation, so that people can deconvolve the whole image using a single PSF without knowing scene depth. Different choices of camera parameters or the PSF to deconvolve with lead to different deblurring qualities. With the derived evaluation criterion, we are able to derive the optimal PSF to deconvolve with in a closed-form and optimize camera parameters for the best deblurring results. Computer science CUCS-007-12 http://hdl.handle.net/10022/AC:P:13017 English NNC NNC 2012-04-17 12:56:17 -0400 2012-04-17 13:05:45 -0400 7019 eng