Nonuniformity correction of infrared images based on bivariate quadratic model

The spatial fixed-pattern noise (FPN) compromises severely the quality of the acquired imagery, even makes such images inappropriate for some applications. In order to lower the FPN, some critical nonuniformity correction (NUC) algorithms such as NUC based on linear model, scene-based NUC and so on have been developed. But each algorithm has some drawbacks: restricted application in small dynamic range of objects temperature, low performance under the drift of the environment temperature and complex calculations. In these cases, we develop a bivariate and quadratic model of the FPA and the NUC technique based on the model. The proposed method does not need any assumptions and is a good solution for hardware implementation. It overcomes the drawbacks of the critical algorithm mentioned above. The last simulations and experiments show that the proposed algorithm exhibits a superior correction effect in both large objects temperature range and environment temperature range.