Study On Adaptive Nonuniformity Correction Algorithm For IRFPA Based On Imaging Guidance Condition

Along with the development of the infrared focal-plane array (IRFPA) technology, the IRFPA imaging technology has also obtained rapid development, and has obtained widespread application in imaging guidance domain. However, the array nonuniformity is still a important limiting factor for the performance of the IRFPA imaging system. Therefore, nonuniformity correction must be implemented in the practical application.Because the nonlinearity of the detectors'response under the large dynamic rank and the slowly and randomly drift with time of the detectors'response, all of the above make the nonuniformity correction become an even more challenging problem. Aiming at the peculiar problems which the IRFPA faces under imaging guidance condition, namely the large dynamic rank of input signal, the complex changeful condition & surroundings and the high work frame-rate and so on, five based-scene algorithms for nonuniformity correction and one technology for project realization are presented in this dissertation, and the bad affect of above problems on nonuniformity correction is solved or reduced. More precisely: Aiming at the application of focal-plane array in the large dynamic rank, a algorithm holding dual correction performance is presented---Kalman filtering algorithm for nonuniformity correction based on quadratic model, and can implement both nonuniformity and nonlinearity correction to IRFPA; Aiming at the application of IRFPA in the special environment of the inadequate scene change, a correction algorithm based on image registration technology is presented. The algorithm does not need to satisfy constant-statistics constrain for various detectors, and has a better convergence; Aiming at the shortage of the traditional correction algorithm based on scene statistics and in order to the actual need, a new correction algorithm based on scene statistics is presented. Comparing it with the typical algorithm based on scene statistics, this algorithm has a better convergence and smaller operation, and need less memories, and is easier to realize. Moreover, this algorithm similarly adapts the nonuniformity correction under the inadequate scene change conditions for IRFP; Aiming at the change of the response characteristic and stability of IRFPA caused by the changeful condition & surroundings, and the shortage which the traditional neural network algorithm eliminates the low-frequency spatial noise, on the basis of wavelet analysis for the frequency characteristic of the spatial noise caused by the nonuniformity, two improved correction algorithms are presented---the correction algorithm based on average and neural network combination, and the correction algorithm based on RLS. Both have the stronger nonuniformity correction ability. Thereinto, the former is simpler. The latter has a better convergence and stronger tracing ability, and can effectively suppress the"ghosting" and the target fade-out phenomenon; Moreover,...