Sergio N. Torres

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A novel statistical approach is undertaken for the adaptive estimation of the gain and bias nonuniformity in infrared focal-plane array sensors from scene data. The gain and the bias of each detector are regarded as random state variables modeled by a discrete-time Gauss-Markov process. The proposed Gauss-Markov framework provides a mechanism for capturing(More)
A statistical algorithm has been developed to compensate for the fixed-pattern noise associated with spatial nonuniformity and temporal drift in the response of focal-plane array infrared imaging systems. The algorithm uses initial scene data to generate initial estimates of the gain, the offset, and the variance of the additive electronic noise of each(More)
What is to our knowledge a new scene-based algorithm for nonuniformity correction in infrared focal-plane array sensors has been developed. The technique is based on the inverse covariance form of the Kalman filter (KF), which has been reported previously and used in estimating the gain and bias of each detector in the array from scene data. The gain and(More)
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(More)
In this paper, a novel automated algorithm to estimate and remove the continuous baseline from measured flame spectra is proposed. The algorithm estimates the continuous background based on previous information obtained from a learning database of continuous flame spectra. Then, the discontinuous flame emission is calculated by subtracting the estimated(More)
The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with a fixed-pattern noise. In this paper we present an enhanced adaptive scene-based non-uniformity correction (NUC) technique. The method simultaneously estimates detector’s parameters and performs the non-uniformity compensation using a neural network(More)
We present an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and(More)
This paper discusses the performance and analysis of a recently published scene-based statistical technique for nonuniformity correction (NUC) of infrared focal plane arrays (FPA). In particular, the method is improved by reducing the number of parameters computing in real-time from three (gain, offset and the standard deviation of the temporal noise) to(More)
A statistical technique for adaptive nonuniformity correction of infrared image sequences has been developed. The method, which relies on our previously developed constant range nonuniformity correction method, estimates the nonuniformity parameters using two recursive estimation techniques. The method selects an estimation algorithm using a decision rule(More)