Strip non-uniformity correction in uncooled long-wave infrared focal plane array based on noise source characterization

In uncooled long-wave infrared (LWIR) imaging systems, nonlinear behaviors of infrared detectors and lack of column cross-calibration generate obvious strip non-uniformity in the captured infrared images. Human observers are particularly sensitive to the resulting high-frequency Fixed Pattern Noise (FPN). In this paper, we propose to learn characteristics of such strip-type FPN through a set of thermal calibration experiments. Our thermal calibration experiments discover that a polynomial curve model can be used to approximate the relationship between infrared data and strip noise of sensor detectors within a column. The derived noise behavioral model allows us to distinguish high-contrast components caused by image texture and strip noise. An effective single-image based processing algorithm is proposed to remove strip-type non-uniformity in infrared images without causing undesired blurring effects. The performance of the proposed technique is thoroughly investigated, and is compared to the state-of-the-art strip denoising algorithm using realistic infrared images.