Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection

Abstract

The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method.