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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.

Ordinary kriging and inverse distance weighted (IDW) are two interpolation methods for spatial analysis of data and are commonly used to analyze macroscopic spatial data in the fields of remote sensing, geography, and geology. In this study, these two interpolation techniques were compared and used to analyze microscopic chemical images created from time of flight-secondary ion mass spectrometry images from a patterned polymer sample of fluorocarbon (C(x)F(y)) and poly(aminopropyl siloxane) (APS, a.k.a. siloxane). Data was eliminated from the original high-resolution data set by successive random removal, and the image file was interpolated and reconstructed with a random subset of points using both methods. The statistical validity of the reconstructed image was determined by both standard geographic information system (GIS) validation statistics and evaluating the resolution across an image boundary using ASTM depth and image resolution methodology. The results show that both ordinary kriging and IDW techniques can be used to accurately reconstruct an image using substantially fewer sample points than the original data set. Ordinary kriging performed better than the IDW technique, resulting in fewer errors in predicted intensities and greater retention of original image features. The size of the data set required for the most accurate reconstruction of the original image is directly related to the autocorrelation present within the data set. When 10% of the original siloxane data set was used for an ordinary kriging interpolation, the resulting image still retained the characteristic gridlike pattern. The C(x)F(y) data set exhibited stronger spatial correlation, resulting in reconstruction of the image with only 1% of the original data set. The removal of data points does result in a loss of image resolution; however, the resolution loss is not directly related to the percentage of sample points removed.

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