Non-local Means-based Nonuniformity Correction for Infrared Focal-Plane Array Detectors

The infrared imaging systems are normally based on the infrared focal-plane array (IRFPA) which can be considered as an array of independent detectors aligned at the focal plane of the imaging system. Unfortunately, every detector on the IRFPA may have a different response to the same input infrared signal which is known as the nonuniformity problem. Then we can observe the fixed pattern noise (FPN) from the resulting images. Standard nonuniformity correction (NUC) methods need to be recalibrated after a short period of time due the temporal drift of the FPN. Scene-based nonuniformity correction (NUC) techniques eliminate the need for calibration by correction coefficients based on the scene being viewed. However, in the scene-based NUC method the problem of ghosting artifacts widely seriously decreases the image quality, which can degrade the performance of many applications such as target detection and track. This paper proposed an improved scene-based method based on the retina-like neural network approach. The method incorporates the use of non-local means (NLM) method into the estimation of the gain and the offset of each detector. This method can not only estimates the accurate correction coefficient but also restrict the ghosting artifacts efficiently. The proposed method relies on the use of NLM method which is a very successful image denoising method. And then the NLM used here can preserve the image edges efficiently and obtain a reliable spatial estimation. We tested the proposed NUC method by applying it to an IR sequence of frames. The performance of the proposed method was compared the other well-established adaptive NUC techniques.