Focusing techniques

TLDR

The study derives camera focusing techniques using a paraxial geometric optics model. The authors implement the techniques on the SPARCS prototype, using a calibration‑free search over camera parameters and energy maximization on various filtered images, and compare their performance experimentally. The techniques are theoretically sound, perform well experimentally, with bandpass‑filter methods excelling under high‑frequency noise, and low‑pass‑filtered image gradient energy is recommended for practical use.

Abstract

We use the paraxial geometric optics model of image formation to derive a set of camera focusing techniques. These techniques do not require calibration of cameras but involve a search of the camera parameter space. The techniques are proved to be theoretically sound under weak assumptions. They include energy maximization of unfiltered, low-pass-filtered, high-pass-filtered, and bandpass-filtered images. It is shown that in the presence of high spatial frequencies, noise, and aliasing, focusing techniques based on bandpass filters perform well. The focusing techniques are implemented on a prototype camera system called the Stonybrook passive autofocusing and ranging camera system (SPARCS). The architecture of SPARCS is described briefly. The performance of the different techniques are compared experimentally. All techniques are found to perform well. The energy of low-pass-filtered image gradient, which has better overall characteristics, is recommended for practical applications.