Global optimization radiometric calibration method for an infrared system with a wide dynamic range

Abstract

Considering the conventional calibration restriction of the complicated calibration procedures, narrow dynamic range, and less correlation in the calibration data, a global optimization radiometric calibration method is proposed in this paper. First, a unified database is generated by integrating different gray-level images, neutral density attenuators, integration times, and target radiations under the deduced infrared physical model. Then, the calibration coefficients are automatically learned through the relative error backward propagation network. Finally, experiments are conducted on a large-aperture ground-based infrared system to evaluate the effectiveness of the proposed method. The results indicate the proposed method can solve the problem of learning imbalance with large fluctuations of infrared radiation, ensure global measurement precision with a simpler calibration procedure, and accurately measure the internal stray radiation of the optical system.