Correction In Focal-plane Arrays Research Articles

Static scene statistical algorithm for nonuniformity correction in focal-plane arrays

A static scene statistical nonuniformity correction ( S 3 NUC ) method was developed based on the higher-order moments of a linear statistical model of a photodetection process. S 3 NUC relieves the requirement for calibrated targets or a moving scene for NUC by utilizing two data sets of different intensities but requires low scene intensity levels. The first-, second-, and third-order moments of the two data sets are used to estimate the gain and bias values for the detectors in a focal-plane array (FPA). These gain and bias values may then be used to correct the nonuniformities between detectors or to initialize other continuous calibration methods. S 3 NUC was successfully applied to simulated data as well as measured data at visible wavelengths.

Total variation approach for adaptive nonuniformity correction in focal-plane arrays

In this Letter we propose an adaptive scene-based nonuniformity correction method for fixed-pattern noise removal in imaging arrays. It is based on the minimization of the total variation of the estimated irradiance, and the resulting function is optimized by an isotropic total variation approach making use of an alternating minimization strategy. The proposed method provides enhanced results when applied to a diverse set of real IR imagery, accurately estimating the nonunifomity parameters of each detector in the focal-plane array at a fast convergence rate, while also forming fewer ghosting artifacts.

An algebraic algorithm for nonuniformity correction in focal-plane arrays.

A scene-based algorithm is developed to compensate for bias nonuniformity in focal-plane arrays. Nonuniformity can be extremely problematic, especially for mid- to far-infrared imaging systems. The technique is based on use of estimates of interframe subpixel shifts in an image sequence, in conjunction with a linear-interpolation model for the motion, to extract information on the bias nonuniformity algebraically. The performance of the proposed algorithm is analyzed by using real infrared and simulated data. One advantage of this technique is its simplicity; it requires relatively few frames to generate an effective correction matrix, thereby permitting the execution of frequent on-the-fly nonuniformity correction as drift occurs. Additionally, the performance is shown to exhibit considerable robustness with respect to lack of the common types of temporal and spatial irradiance diversity that are typically required by statistical scene-based nonuniformity correction techniques.

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.