The Research On Scene-Based Nonuniformity Correction Algorithm Of Infrared Focal Plane Array

Now Infrared Focal Plane Array (IRFPA) is the main detector in infrared imaging system. But the nonuniformity response existed in IRFPA restricts the detection performance of the system greatly. Real time and effective of the Nonuniformity Correction algorithm become the key technique of studying infrared imaging system. Discussion and study of nonuniformity response method is deeply developed in this paper in detail. And the keystone is the study of Nonuniformity Correction algorithm based the scene.In this paper firstly the elements of the nonuniformity in the IRFPA is summarize; secondly the generation principle of the nonuniformity in the IRFPA and the characteristics of the infrared imaging system response are analyzed. Analysis is focused on the nonlinear response curve model of infrared focal plane arrays. "S" curve response model replaces the linear response model and makes it more similar to the true response of detector module.The IRFPA Nonuniformity Correction algorithm based on the two-point temperature picketage is used in the scene. The least squares fitting Nonuniformity Correction algorithm based on scene is held out. The least squares fitting correction can directly be carried on using any moving scene by the algorithm. The picketage is not necessary with standard reference source in advance. Some measurable assessment indexes were given to the result of the Nonuniformity Correction. The algorithm's effectiveness and superiority were verified by simulation.After the research on Nonuniformity Correction in IRFPA base on artificial neural networks, variable learning rate backpropagation and classic learning step-length backpropagation are put forward. Variable learning rate backpropagation could change learning rate of backpropagation to quicken the speed to get stable in peculiar situation. Classic learning step-length backpropagation selects the prime step-length by golden section search method, and could achieve the expect result in the least times of iteration. The effectiveness and superiority of two algorithms were verified by simulation.Simulation experiments were performed. The comparison is done among three above-mentioned algorithms, the effectiveness and adaptability of the three algorithms are validated by the simulation experiments.