FPGA-based methodology for depth-of-field extension in a single image

Abstract

Computer vision applications rely upon high resolution images with extended depth of field (DoF). Most approaches contain arrays of lenses and computing intensive algorithms that must be calibrated every time, to reach in-focus images; however, by changing directly the system focal length, resolution and information are lost. Traditional methods consist in taking a great number of images varying the optical system pupil aperture, whereas, the post processing system demands a great amount of computational resources with long processing time and high implementation cost. In this work a novel methodology for DoF extension that applies a complex-amplitude mask during a single image pre-processing taken at full pupil aperture, and a Wiener filter for the image recovery without focalization errors, during post-processing, is introduced. An FPGA-based implementation shows the feasibility of the proposed methodology for real-time DoF extension. Obtained results demonstrate qualitatively and quantitatively the effectiveness of the proposed FPGA-based method, which offers a reconfigurable solution for online DoF extension on a single image, in real time.