Minor Distortions with Major Consequences: Correcting Distortions in Imaging Spectrographs

Abstract

Projective transformation is a mathematical correction (implemented in software) used in the remote imaging field to produce distortion-free images. We present the application of projective transformation to correct minor alignment and astigmatism distortions that are inherent in dispersive spectrographs. Patterned white-light images and neon emission spectra were used to produce registration points for the transformation. Raman transects collected on microscopy and fiber-optic systems were corrected using established methods and compared with the same transects corrected using the projective transformation. Even minor distortions have a significant effect on reproducibility and apparent fluorescence background complexity. Simulated Raman spectra were used to optimize the projective transformation algorithm. We demonstrate that the projective transformation reduced the apparent fluorescent background complexity and improved reproducibility of measured parameters of Raman spectra. Distortion correction using a projective transformation provides a major advantage in reducing the background fluorescence complexity even in instrumentation where slit-image distortions and camera rotation were minimized using manual or mechanical means. We expect these advantages should be readily applicable to other spectroscopic modalities using dispersive imaging spectrographs.