Scene-Based Nonuniformity Correction with Reduced Ghosting Using a Gated LMS Algorithm

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.

Regularization approach to scene-based nonuniformity correction

“…1. The eight images are generated by adding the artificial offset 9,15 to calibrated infrared images caputured by a 320 × 256 InSb focal plane array camera operating in the 3 to 5 μm range. The RNU is generally composed of two patterns, the lowfrequency one and white noise-like one as reported in Ref.…”

“…24 Therefore, the artificial offset is generated as realizations of independent identically distributed Gaussian random variables. 9 We plot the proposed energy function in Eq. (2) against the number of iterations.…”

“…7,9 In other words, the offset remains almost constant for several consecutive image frames. 20 This regularization term favors the offset with small changes along the time axis.…”

“…In general, SBNUC schemes can be broadly divided into two categories: constant statistics (CS) methods [4][5][6] and least mean square (LMS) methods. [7][8][9][10] The original CS method assumes that the temporal mean and standard deviation of each pixel are constant over time and space. 5 The performance of the original CS method is reliable as long as the assumption is valid.…”

“…This method is further improved in Ref. 9 to remove burn-in artifacts caused by temporally slowlyvarying image regions. An approach proposed in Ref.…”