Regular paper

Quantitative characterization of super-resolution infrared imaging based on time-varying focal plane coding

¹ School of Physics and Optoelectronic Engineering, Xidian University, Xi’an, Shaanxi, 710071, China
² Shaanxi Zhong Tian Rocket Technology Limited Company, Xi’an, Shaanxi, 710025, China
³ Science and Technology on Low-Light-Level Night Vision Laboratory, Xi’an, Shaanxi, 710065, China

^* xrwang@mail.xidian.edu.cn

Received: 25 May 2014
Revised: 12 September 2014

Abstract

High resolution infrared image has been the goal of an infrared imaging system. In this paper, a super-resolution infrared imaging method using time-varying coded mask is proposed based on focal plane coding and compressed sensing theory. The basic idea of this method is to set a coded mask on the focal plane of the optical system, and the same scene could be sampled many times repeatedly by using time-varying control coding strategy, the super-resolution image is further reconstructed by sparse optimization algorithm. The results of simulation are quantitatively evaluated by introducing the Peak Signal-to-Noise Ratio (PSNR) and Modulation Transfer Function (MTF), which illustrate that the effect of compressed measurement coefficient r and coded mask resolution m on the reconstructed image quality. Research results show that the proposed method will promote infrared imaging quality effectively, which will be helpful for the practical design of new type of high resolution infrared imaging systems.