A Spatially Invariant Noise Model for Minimum Noise Fraction (MNF) Denoising of Hyperspectral Datasets: Applications to Large-Scale Infrared Spectroscopic Pathology

Abstract

Use of Minimum Noise Fraction (MNF) denoising, previously developed for remote sensing applications, is an increasingly popular denoising technique for Infrared (IR) imaging data. The original MNF method proposed by Green et al. along with the faster ‘Fast MNF’ and resolution independent ‘MNF2’ all use a noise correlation matrix calculated based on neighbouring pixels, creating a heavy order-dependence. This approach fails when the spatial relationship between pixels is disrupted, for example, when large images cannot be loaded into memory on a standard workstation and are thus processed in patches or tissue data extracted using masking. We propose a spatially invariant MNF denoising method (iMNF) that uses a non-uniform, physically motivated noise estimation profile that removes this order-dependence, resulting in a robust, spatially invariant MNF based denoising algorithm. This allows for the application of the MNF denoising application to datasets where the spatial assumption is likely to be weakened by use of masking, or for unordered data such as randomly drawn labelled data, patch-wise segmentations of large scaler images, or single-point spectral collections. This application was tested on representative prostate tissue biopsies for their spatial and chemical heterogeneity. Results indicate a robust, spatially invariant denoiser that is comparable to the Fast MNF method for structured and loosely structured data but is superior for unstructured data. This removes a critical bottleneck in the analysis pipeline for large IR images, such as those required in spectral pathology.