An improved retina-like nonuniformity correction for infrared focal-plane array

Abstract The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with nonuniformity noise. This paper mainly proposes an improved adaptive nonuniformity correction (NUC) method based on the retina-like neural network approach. The main purpose of NUC method is to obtain reliable estimations of gain and offset parameters. In this paper the two correction parameters are updated with two different learning rates respectively for the purpose of updating these two parameters synchronously. And then more accurate estimations of the two correction parameters can be obtained. Again, in order to reduce the ghost artifacts normally introduced by the strong edge effectively, the proposed algorithm employs the non-local means (NLM) method to estimate the desired target value of each detector. The proposed NUC method has been tested by applying it to the IR sequence of frames with simulated nonuniformity noise and real nonuniformity noise, respectively. The performance comparisons are implemented with the well-established scene-based NUC techniques. And the experimental results show the efficiency of the proposed method.