High-resolution calibration and data reconstruction method for image mapping spectrometer

Abstract

The image mapping spectrometer (IMS) is a kind of snapshot spectral imaging system that modulates the 3D spatial-spectral datacube (x,y,λ) to a 2D sensor for simultaneous measurement. Since the IMS utilizes an image mapper for mapping the spatial information, a calibration method is required to generate the lookup table between the measured raw image pixels and the desired datacube voxels. However, the current calibration methods are either time-consuming or low-accurate. We present a combined slit scan calibration and data processing approach that can balance the calibration efficiency and accuracy, by taking in account the irregularity of field of view (FOV) of the image mapper in the calibrating setup. Additionally, we analyze the spatial-spectral mixing phenomenon of the IMS datacube recovered from the calibrated lookup table. To reconstruct high-quality spectral datacube from the spatial-spectral mixed datacube, a spatial-spectral unmixing network (SSUNet) based on multi-head multi-attention mechanism is presented. Experimental results validated the effectiveness of both the calibration strategy and the datacube restoration network.