Efficient Scene-based Nonuniformity Correction and Enhancement

We propose a unified framework for scene-based nonuniformity correction (NUC) and enhancement that is required for the FPA-like (focal plane array) sensors to remove fixed-pattern noise and to enhance the image quality. In contrast to existing scene-based NUC methods, the new framework allows us to process image sequences under severe and structured nonuniformity efficiently and to obtain high quality images. We achieve this goal by applying an efficient registration-based method that is bootstrapped by statistical scene-based NUC methods. Specifically, we initialize the whole NUC-enhancement process by applying statistical methods in order to obtain images with quality just enough for image registration. To obtain high-quality images, we integrate the NUC process with super-resolution techniques to reduce noise and enhance resolution. This is achieved by adopting a new imaging model that includes linear NUC model and image blurring and subsampling. Experiments with real data demonstrate the efficacy of the proposed framework.