L-Probe: New Approach for Spike Removal in Raman Hyperspectral Imaging Based on Local Prominence Peak Filtering

Abstract

Raman hyperspectral imaging is an indispensable analytical technique that can provide simultaneous chemical and spatial information of a sample, enabling a higher comprehension and sampling of the analyzed material. However, a known challenge in processing large hyperspectral Raman datasets is the effective removal of spurious, high-intensity peaks caused by cosmic rays (cosmic spikes). Correcting cosmic spikes in complex hyperspectral images is particularly difficult due to the sheer volume of data, the inherent chemical heterogeneity of pixels, variations in background intensity, and the shape of authentic Raman bands. To address this, we propose a novel cosmic spike correction algorithm specifically engineered for Raman hyperspectral imaging. The L-probe algorithm employs the concept of local prominence for the reliable identification of suspicious peaks and spike removal based on the assumption of chemical composition homogeneity across adjacent pixels, allowing targeted and localized correction. The algorithm was rigorously evaluated using a factorial design of experiments (DoE) applied to an artificial hyperspectral image to establish its parameters. Final validation was successfully performed using real hyperspectral images acquired from complex, phase-separated samples, such as cosmetic emulsions, demonstrating the robustness of the algorithm.