A new nonuniformity correction algorithm for infrared line scanners

Nonuniformity correction (NUC) is a critical task for achieving higher performances in modern infrared imaging systems. The striping fixed pattern noise produced by the scanning-type infrared imaging system can hardly be removed clearly by many scene-based non-uniformity correction methods, which can work effectively for staring focal plane arrays (FPA). We proposed an improved nonuniformity algorithm that corrects the aggregate nonuniformity by two steps for the infrared line scanners (IRLS). The novel contribution in our approach is the integration of local constant statistics (LCS) constraint and neural networks. First, the nonuniformity due to the readout electronics is corrected by treating every row of pixels as one channel and normalizing the channel outputs so that each channel produces pixels with the same mean and standard deviation as median value of the local channels statistics. Second, for IRLS every row is generated by pushbrooming one detector on line sensors, we presume each detector has one neuron with a weight and an offset as correction parameters, which can update column by column recursively at Least Mean Square sense. A one-dimensional median filter is used to produce ideal output of linear neural network and some optimization strategies are added to increase the robustness of learning process. Applications to both simulated and real infrared images demonstrated that this algorithm is self-adaptive and able to complete NUC by only one frames. If the nonuniformity is not so severe then only the first step can obtain a good correction result. Combination of two steps can achieve a higher correction level and remove stripe pattern noise clearly.