Variable-step constant statistics algorithm for removing residual fixed pattern noise of infrared images as second non-uniformity correction

Regarding the appearance of fixed pattern noise (FPN) in the IR images of an IR observation system introduced by errors in assembly, environment, etc. Non-Uniformity Correction (NUC) is an important technique for IRFPA. Because the real radiation response of pixels in the given dynamic range is nonlinear and the existence of 1/f noise, especially the high temperature scaling point changes the thermal balance of the IR observation system, using the traditional linear approximate method (temperature scaling method) is hard to obtain the perfect corrective images. On the other hand, because of Scene-Based Non-Uniformity Correction (SBNUC) does not rely on specialized hardware, SBNUC is very attractive alternative to radiometric calibration for infrared sensors, thereinto, Constant Statistics (CS) is the best known approach, but it lies on the scene content and has intimate correlation with the sample quantity. So, in this paper, we present a novel approach which inherits the rapidity of temperature scaling method and also consider the astringency of CS, using variable-step constant statistics (VSCS) as second non-uniformity correction in the spatial and time domains of infrared images to eliminate the residual fixed pattern noise which resulted from the theoretical and methodological errors of temperature scaling method. The experimental result for the real infrared images data is a solution which effectively eliminates the residual fixed pattern noise, and at the same time, it proved the effectiveness of this algorithm.