Image denoising techniques applied to glow discharge optical emission spectroscopy elemental mapping

Abstract

Glow discharge optical emission spectroscopy (GDOES) is becoming a mature technique for depth profiling analysis. The advantages it affords, including fast, quantitative, and multi-elemental analysis, as well as allowing very high depth-resolution, have attracted the attention of the thin film community. Recently, the use of GDOES under pulsed-mode operation and coupled to hyper-spectral imaging techniques has been proposed to perform surface elemental mapping. Several manuscripts have reported on the underlying mechanisms in GDOES pertaining to the spatial resolution, while other manuscripts have reported on elemental mapping applications, for example, regarding separated proteins or thin film combinatorial libraries. Only a couple of studies have reported image processing techniques applied to GDOES elemental mapping and none having to do with image denoising purposes. Herein, image denoising techniques are compared in several scenarios: (a) mapping of homogeneous samples; (b) mapping of heterogeneous samples in two dimensions; (c) mapping of heterogeneous samples in three dimensions. Denoising techniques compared include averaging, median filtering, principal component analysis (PCA), and local pixel grouping-PCA. The peak signal-to-noise ratio is used to show the efficiency of noise removal, while the full-with-half-maximum of emission from sharp features is used to demonstrate the resolution degradation effects of each denoising technique. In general, it is observed that PCA outperforms other techniques albeit with a higher cost of image processing time. Also, it becomes evident that having multiple image slices (a 3^rd dimension) affords more efficient noise removal while minimizing losses in spatial resolution.