Infrared images are commonly afflicted by distortions such as non-uniformity. Non-uniformity is characterized by horizontal and vertical fixed pattern noise. Accurately estimating the amount of non-uniformity present in an image and removing that amount of non-uniformity noise are open problems. Several estimators of non-uniformity exist, but their ability to estimate degrades with the presence of other sources of noise. Specifically, most of these metrics lack the robustness demanded by a more complete non-uniformity model. Previous non-uniformity correction algorithms are compared and found to underperform relative to a more complete model of non-uniformity that we have developed. Using this model, we have created a new denoising algorithm, which we call the Gaussian scale mixture perceptual pattern denoiser. The new model and algorithm can fully characterize non-uniformity using covariance matrices.