Statistical algorithm for nonuniformity correction in focal-plane arrays

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 detector element. The algorithm then updates these parameters by use of subsequent frames and uses the updated parameters to restore the true image by use of a least-mean-square error finite-impulse-response filter. The algorithm is applied to infrared data, and the restored images compare favorably with those restored by use of a multiple-point calibration technique

Scene-based nonuniformity correction with video sequences and registration

We describe a new, to our knowledge, scene-based nonuniformity correction algorithm for array detectors. The algorithm relies on the ability to register a sequence of observed frames in the presence of the fixed-pattern noise caused by pixel-to-pixel nonuniformity. In low-to-moderate levels of nonuniformity, sufficiently accurate registration may be possible with standard scene-based registration techniques. If the registration is accurate, and motion exists between the frames, then groups of independent detectors can be identified that observe the same irradiance (or true scene value). These detector outputs are averaged to generate estimates of the true scene values. With these scene estimates, and the corresponding observed values through a given detector, a curve-fitting procedure is used to estimate the individual detector response parameters. These can then be used to correct for detector nonuniformity. The strength of the algorithm lies in its simplicity and low computational complexity. Experimental results, to illustrate the performance of the algorithm, include the use of visible-range imagery with simulated nonuniformity and infrared imagery with real nonuniformity

A MAP Estimator for Simultaneous Superresolution and Detector Nonunifomity Correct

During digital video acquisition, imagery may be degraded by a number of phenomena including undersampling, blur, and noise. Many systems, particularly those containing infrared focal plane array (FPA) sensors, are also subject to detector nonuniformity. Nonuniformity, or fixed pattern noise, results from nonuniform responsivity of the photodetectors that make up the FPA. Here we propose a maximuma posteriori (MAP) estimation framework for simultaneously addressing undersampling, linear blur, additive noise, and bias nonuniformity. In particular, we jointly estimate a superresolution (SR) image and detector bias nonuniformity parameters from a sequence of observed frames. This algorithm can be applied to video in a variety of ways including using amoving temporal window of frames to process successive groups of frames. By combining SR and nonuniformity correction (NUC) in this fashion, we demonstrate that superior results are possible compared with the more conventional approach of performing scene-based NUC followed by independent SR. The proposed MAP algorithm can be applied with or without SR, depending on the application and computational resources available. Even without SR, we believe that the proposed algorithm represents a novel and promising scene-based NUC technique. We present a number of experimental results to demonstrate the efficacy of the proposed algorithm. These include simulated imagery for quantitative analysis and real infrared video for qualitative analysis

Simultaneous temperature estimation and nonuniformity correction from multiple frames

Infrared (IR) cameras are widely used for temperature measurements in various applications, including agriculture, medicine, and security. Low-cost IR camera have an immense potential to replace expansive radiometric cameras in these applications, however low-cost microbolometer-based IR cameras are prone to spatially-variant nonuniformity and to drift in temperature measurements, which limits their usability in practical scenarios. To address these limitations, we propose a novel approach for simultaneous temperature estimation and nonuniformity correction from multiple frames captured by low-cost microbolometer-based IR cameras. We leverage the physical image acquisition model of the camera and incorporate it into a deep learning architecture called kernel estimation networks (KPN), which enables us to combine multiple frames despite imperfect registration between them. We also propose a novel offset block that incorporates the ambient temperature into the model and enables us to estimate the offset of the camera, which is a key factor in temperature estimation. Our findings demonstrate that the number of frames has a significant impact on the accuracy of temperature estimation and nonuniformity correction. Moreover, our approach achieves a significant improvement in performance compared to vanilla KPN, thanks to the offset block. The method was tested on real data collected by a low-cost IR camera mounted on a UAV, showing only a small average error of $0.27^\circ C-0.54^\circ C$ relative to costly scientific-grade radiometric cameras. Our method provides an accurate and efficient solution for simultaneous temperature estimation and nonuniformity correction, which has important implications for a wide range of practical applications

Multi-Model Kalman Filtering for Adaptive Nonuniformity: Correction in Infrared Sensors

This paper presents 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 the bias matrices of the sensor, according to its own dynamical-model parameters, which underly the statistics of the scene and the nonuniformity as well as the temporal drift in the nonuniformity. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are obtained according to the a posteriori -likelihood principle, applied to the family of models by considering the output residual errors associated with each filter. These weights are updated iteratively between blocks of data, providing the estimator the means to follow the dynamics of the scenes and the sensor. The performance of the proposed estimator and its ability to compensate for fixed-pattern noise are tested using both real and simulated data. The real data is obtained using two cameras operating in the mid- and long-wave infrared regime

Generalized Algebraic Algorithm for Scene-based Nonuniformity Correction

ABSTRACT This paper presents an overview of three recently developed scene-based nonuniformity correction techniques, namely, the algebraic scene-based algorithm (ASBA), the extended radiometrically accurate scene-based algorithm (RASBA) and the generalized algebraic scene-based algorithm (GASBA). The ASBA uses pairs of image frames that exhibit one-dimension sub-pixel motion to algebraically extract estimates of bias nonuniformity. The RASBA incorporates arbitrary sub-and super-pixel two-dimensional motion in conjunction with limited perimeter-only absolute calibration to obtain radiometrically accurate estimates of the bias nonuniformity. The RASBA provides the advantage of being able to maintain radiometry in the interior photodetectors without interrupting their operation. The GASBA is a generalized non-radiometric form of the algorithm that uses image pairs with arbitrary two-dimensional motion and encompasses both the ASBA and RASBA algorithms. This generalization is accomplished by initially guaranteeing bias uniformity in the perimeter detectors. This uniformity can be achieved by first applying the ASBA estimates. The generalized algorithm is then able to automatically maintain perimeter uniformity without the need for re-application of the ASBA. Thus, the GASBA is able to operate completely in a non-radiometric mode, alleviating the need for the perimeter calibration system if desired. The generalized algorithm is applied to real infrared imagery obtained from both cooled and uncooled infrared cameras. A hardware implementation of the proposed algorithm will also be discussed along with several ongoing commercial applications of the technology

Static Scene Statistical Non-Uniformity Correction

Non-Uniformity Correction (NUC) is required to normalize imaging detector Focal-Plane Array (FPA) outputs due to differences in the end-to-end photoelectric responses between pixels. Currently, multi-point NUC methods require static, uniform target scenes of a known intensity for calibration. Conversely, scene-based NUC methods do not require a priori knowledge of the target but the target scene must be dynamic. The new Static Scene Statistical Non-Uniformity Correction (S3NUC) algorithm was developed to address an application gap left by current NUC methods. S3NUC requires the use of two data sets of a static scene at different mean intensities but does not require a priori knowledge of the target. The S3NUC algorithm exploits the random noise in output data utilizing higher order statistical moments to extract and correct fixed pattern, systematic errors. The algorithm was tested in simulation and with measured data and the results indicate that the S3NUC algorithm is an accurate method of applying NUC. The algorithm was also able to track global array response changes over time in simulated and measured data. The results show that the variation tracking algorithm can be used to predict global changes in systems with known variation issues

Static Scene Statistical Non-Uniformity Correction

Non-Uniformity Correction (NUC) is required to normalize imaging detector Focal-Plane Array (FPA) outputs due to differences in the end-to-end photoelectric responses between pixels. Currently, multi-point NUC methods require static, uniform target scenes of a known intensity for calibration. Conversely, scene-based NUC methods do not require a priori knowledge of the target but the target scene must be dynamic. The new Static Scene Statistical Non-Uniformity Correction (S3NUC) algorithm was developed to address an application gap left by current NUC methods. S3NUC requires the use of two data sets of a static scene at different mean intensities but does not require a priori knowledge of the target. The S3NUC algorithm exploits the random noise in output data utilizing higher order statistical moments to extract and correct fixed pattern, systematic errors. The algorithm was tested in simulation and with measured data and the results indicate that the S3NUC algorithm is an accurate method of applying NUC. The algorithm was also able to track global array response changes over time in simulated and measured data. The results show that the variation tracking algorithm can be used to predict global changes in systems with known variation issues