Chemometric methods applied in the image plane to correct striping noise in hyperspectral chemical images of biomaterials

Abstract

Array detectors improve data collection speed in hyperspectral chemical imaging, yet are prone to striping noise patterns in the image plane, which is difficult to remove. This type of noise affects spectral features and disturbs visual impression of the scene. We found that this type of noise depends on the material composition and setting parameters, ie, pixel size, and it also varies in accordance with the signal intensity of the observed wavelength. To address this, we proposed a new correction method on the basis of the application of chemometric techniques in the image plane of each wavelength. To verify the effectiveness of this method, infrared transmission images of the 2″ × 2″ positive, 1951 USAF Hi-Resolution Target and biomaterial samples were obtained with a 16-element (8 × 2) pixel array detector. Point detector images of some samples were also acquired and used as reference images. The proposed correction method produced substantial improvements in the visual impression of intensity images. Principal components analysis was performed to inspect spectral changes after preprocessing, and the results suggested that the major spectral features were not altered while the stripes on intensity images were removed. Spectral profiles and principal components analysis loadings inspection confirmed the smoothing ability of this correction method. As traditional preprocessing techniques, standard normal variate and derivative transformation were not able to remove line artifacts, especially on the biomaterial images. Overall, the proposed method was effective for removing striping noise patterns from infrared images with a minimal alteration of the valuable hyperspectral image information.