In uncooled LWIR microbolometer imaging systems, temperature fluctuations of FPA (Focal Plane Array) as well as lens and mechanical components placed along the optical path result in thermal drift and spatial non-uniformity. These non-idealities generate undesirable FPN (Fixed-Pattern-Noise) that is difficult to remove using traditional, individual shutterless and TEC-less (Thermo-Electric Cooling) techniques. In this paper we introduce a novel single-image based processing approach that marries the benefits of both statistical scene-based and calibration-based NUC algorithms, without relying neither on extra temperature reference nor accurate motion estimation, to compensate the resulting temperature-dependent non-uniformities. Our method includes two subsequent image processing steps. Firstly, an empirical behavioral model is derived by calibrations to characterize the spatio-temporal response of the microbolometric FPA to environmental and scene temperature fluctuations. Secondly, we experimentally establish that the FPN component caused by the optics creates a spatio-temporally continuous, low frequency, low-magnitude variation of the image intensity. We propose to make use of this property and learn a prior on the spatial distribution of natural image gradients to infer the correction function for the entire image. The performance and robustness of the proposed temperature-adaptive NUC method are demonstrated by showing results obtained from a 640×512 pixels uncooled LWIR microbolometer imaging system operating over a broad range of temperature and with rapid environmental temperature changes (i.e. from –5°C to 65°C within 10 minutes).