A non-uniformity correction algorithm of infrared image sequences based on constant-statistics

For infrared focal-plane array imaging system, scene-based non-uniformity correction is key technique to deal with fixed pattern noise. Existing algorithms are mainly restricted by convergence speed and ghosting artifacts. In this paper, a novel adaptive scene-based non-uniformity correction technique is presented, which is based on constant-statistics method (CS). Utilizing temporal statistics of infrared image sequences, the proposed method applies an alpha-trimmed mean filter to estimate detector parameters and minimize sample asymptotic variance estimate. Performance of proposed technique is evaluated by simulation and real non-uniformity image. Experimental results show the proposed method inherits characteristics of fast convergence of CS method and increases peak signal to noise ratio by 44.5% and 32.9% respectively, and image ghost problem is improved obviously.