Kernel regression based infrared image non-uniformity correction

Abstract

In traditional scene-based non-uniformity correction methods, ghosting artifacts and image blurring affect the response uniformity of the infrared focal plane array imaging system seriously and decrease the image quality. In order to suppress artifacts ghosting and improve image quality, this paper proposed a new based on kernel regression nonuniformity correction method for infrared image, because of its powerful ability to estimating. The main purpose of proposed method is to obtain reliable estimations of gain and offset parameters. Firstly, in order to suppress the ghost artifacts normally introduced by the strong edge effectively, this paper employs the kernel regression method to estimate the desired pixel value of each detector uint. Then the two correction parameters are achieved with the steepest descent method for the purpose of updating these two parameters synchronously. Finally, more accurate estimations of the two correction parameters can be obtained. Several simulated infrared image sequences are utilized to verify the performance of the proposed method. The results show that our method performs better than other compared methods.