Image fusion combining SEM and ToF-SIMS images

Abstract

a Image fusion allows for the combination of an image containing chemical information but low spatial resolution with a highspatial resolution image having little to no chemical information. The resulting hybrid image retains all the information from the chemically relevant original image, with improved spatial resolution allowing for visual inspection of the spatial correlations. In this research, images were obtained from two sample test grids: one of a copper electron microscope grid with a letter 'A' in the center (referred to below as the 'A-grid'), and the other a Tantalum and Silicon test grid from Cameca that had an inscribed letter 'C' (referred to below as the 'Cameca grid'). These were obtained using scanning electron microscopy (SEM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). Image fusion was implemented with the Munechika algorithm. The edge resolution of the resulting hybrid image was calculated compared with the edge resolution obtained for both the individual ToF-SIMS and SEM images. The challenges of combining complimentary datasets from different instrumental analytical methods are discussed as well as the advantages of having a hybrid image. The distance across the edge for hybrid images of the A-Grid and the Cameca grid were determined to be 21 μm and 8 μm, respectively. When these values were compared to the original ToF-SIMS, SEM and optical microscopy measurements, the fused image had a spatial resolution nearly equal to that obtained in the SEM image for both samples.

Correlative Microscopy Combining Secondary Ion Mass Spectrometry and Electron Microscopy: Comparison of Intensity–Hue–Saturation and Laplacian Pyramid Methods for Image Fusion

“…Note also the absence of the dark regions in the center of the SIMS micrographs (red arrow). The Munechika algorithm, ,− which has been shown to be able to improve the spatial resolution in SIMS EM-sharpening, yields a loss of overall brightness compared to the input image, which can be counteracted by histogram matching at the cost of a reduced contrast bandwidth (f,i). In contrast, LPF conserves the visual appearance of the RGB data: color distortions are reduced while a high contrast bandwidth is preserved (e,h), and the fine structure remains visible (yellow circles).…”

Enhanced EDX images by fusion of multimodal SEM images using pansharpening techniques

“…In Milillo et al . (), SEM and ToF‐SIMS images are combined to increase the spatial resolution of the ToF‐SIMS modality. Thus, to initiate our work, we decided to take inspiration from the work of fusion of information on multi/hyperspectral imaging which is much more abundant and especially on pansharpening techniques (Vivone et al ., ; Loncan et al ., ).…”

“…In Wortmann (2009) andAngelov et al (2016) atomic force microscopy and SEM are combined to provide a view of the topography and material properties of the sample. In Milillo et al (2015), SEM and ToF-SIMS images are combined to increase the spatial resolution of the ToF-SIMS modality. Thus, to initiate our work, we decided to take inspiration from the work of fusion of information on multi/hyperspectral imaging which is much more abundant and especially on pansharpening techniques (Vivone et al, 2015;Loncan et al, 2015).…”

Multimodal image fusion with SIMS: Preprocessing with image registration

“…16 Other aspects which need to be addressed include the relative intensity scales between the input images and ensuring that the input images are registered prior to fusion. 17 For image fusion to become a more widespread data processing technique for SIMS, it must be ensured that artifacts stemming from misaligned input images will not be introduced. These color artifacts are avoided by incorporating image registration, a necessary preprocessing step for performing image fusion.…”