Neural Network Non-uniformity Correction for Infrared Focal Plane Array Based on Perona Malik Diffusion

A new Neural Network Non-Uniformity Correction (PM-NN-NUC) algorithm is proposed for InfraRed Focal Plane Array (IRFPA) based on Perona Malik (PM) diffusion for the situation of degradation model both containing fix pattern noise and Gaussian noise in infrared image. A minimize model is established concerning Non-Uniformity Correction (NUC). It can be seen that PM-NN-NUC uses a similarity in the filtering process on Neural Network Non-Uniformity Correction and PM diffusion, and not only generates the expectation directly but also calculates the iterative step. Correction coefficient reacts on PM diffusion process and combines with PM diffusion and Neural Network Non-Uniformity Correction uniformly. The results of real infrared thermal image show that the proposed algorithm eliminates the fixed pattern noise effectively, but also has excellent performance for the image degraded with fade-out.