Fast Adaptive Nonuniformity Correction for Infrared Focal-Plane Array Detectors

Abstract

<p/> <p>A novel adaptive scene-based nonuniformity correction technique is presented. The technique simultaneously estimates detector parameters and performs the nonuniformity correction based on the retina-like neural network approach. The proposed method includes the use of an adaptive learning rate rule in the gain and offset parameter estimation process. This learning rate rule, together with a reduction in the averaging window size used for the parameter estimation, may provide an efficient implementation that should increase the original method's scene-based ability to estimate the fixed-pattern noise. The performance of the proposed algorithm is then evaluated with infrared image sequences with simulated and real fixed-pattern noise. The results show a significative faster and more reliable fixed-pattern noise reduction, tracking the parameters drift, and presenting a good adaptability to scene changes and nonuniformity conditions.</p

Regularization approach to scene-based nonuniformity correction

“…Objective results for the proposed method are provided in Table 2. We employ a roughness metric 8,9,15 which is defined by…”

“…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.…”

Scene-based non-uniformity correction: From algorithm to implementation on a smart camera

“…On the other hand, statistical techniques model the FPN as a random spatial noise and estimate the statistics of the noise to remove it [41,42,43,44]. Compared with registration-based methods, statistical approaches have been more widely studied because of their relatively lower computational complexity, smaller storage demands, and better realtime performance [44].…”

Improved interframe registration based nonuniformity correction for focal plane arrays

“…Normally, larger values for a can provide a faster convergence speed, but smaller values can assure better stability instead. The adaptive step size strategies are widely adopted to improve the performance of the Scribner's method [17,19,20]. The main idea behind these strategies is to adjust the step size based on local spatial variance of the observed image.…”