title: Improved neural network based scene-adaptive nonuniformity correction method for infrared focal plane arrays.
abstract: An improved scene-adaptive nonuniformity correction (NUC) algorithm for infrared focal plane arrays (IRFPAs) is proposed. This method simultaneously estimates the infrared detectors' parameters and eliminates the nonuniformity causing fixed pattern noise (FPN) by using a neural network (NN) approach. In the learning process of neuron parameter estimation, the traditional LMS algorithm is substituted with the newly presented variable step size (VSS) normalized least-mean square (NLMS) based adaptive filtering algorithm, which yields faster convergence, smaller misadjustment, and lower computational cost. In addition, a new NN structure is designed to estimate the desired target value, which promotes the calibration precision considerably. The proposed NUC method reaches high correction performance, which is validated by the experimental results quantitatively tested with a simulative testing sequence and a real infrared image sequence.
authors: Department of Microelectronics, Xidian University, No. 2 South Taibai Road, Xi'an, Shannxi 710071, China. rlai@mail.xidian.edu.cn
authors: Rui
authors: R
authors: Lai
authors: Yin-tang
authors: YT
authors: Yang
authors: Duan
authors: D
authors: Zhou
authors: Yue-jin
authors: YJ
authors: Li
mesh_terms: D000465:Algorithms; D016477:Artifacts; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007259:Infrared Rays; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013055:Spectrophotometry, Infrared / Q000379:methods*