Abstract
In this paper, we present a scene-based nouniformity correction (NUC) method using a modified adaptive least mean square (LMS) algorithm with a novel gating operation on the updates. The gating is designed to significantly reduce ghosting artifacts produced by many scene-based NUC algorithms by halting updates when temporal variation is lacking. We define the algorithm and present a number of experimental results to demonstrate the efficacy of the proposed method in comparison to several previously published methods including other LMS and constant statistics based methods. The experimental results include simulated imagery and a real infrared image sequence. We show that the proposed method significantly reduces ghosting artifacts, but has a slightly longer convergence time.
Highlights
Detector nonuniformity is a phenomenon adversely affecting many imaging systems, infrared systems [1]
We present a scene-based nouniformity correction (NUC) method using a modified adaptive least mean square (LMS) algorithm with a novel gating operation on the updates
We show that the proposed method significantly reduces ghosting artifacts, but has a slightly longer convergence time
Summary
Detector nonuniformity is a phenomenon adversely affecting many imaging systems, infrared systems [1]. Scene-based methods are attractive because they do not require halting the normal camera operating for periodic calibrations and do not require uniform calibration targets Rather, these methods exploit motion in the acquired video to estimate the nonuniformity parameters and correct the imagery. A Kalman filter based approach has been proposed in [5] that uses the constant range assumption Another class of SBNUC techniques use a least mean square (LMS) algorithm to adaptively determine the nonuniformity model parameters based on a “desired” image that is formed using a spatial low pass filter [6,7,8].
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