Aperture Evaluation for Defocus Deblurring and Extended Depth of Field

For a given camera setting, scene points that lie outside of depth of field (DOF) will appear defocused (or blurred). Defocus causes a loss in image details. To recover details from a defocused region, deblurring techniques must be employed. It is well known that 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 find 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 for 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 that are less sensitive to depth variation, so that a single PSF can be used to deblur captured images without knowledge of scene depth. The deblurring quality for EDOF cameras depends on the parameters of the camera and the PSF used for deconvolution. We show that our evaluation criterion can be used to find both optimal values for the camera parameters and an optimal PSF for deblurring.