An inpainting and super resolution method for image mapping spectrometer

Abstract

Image Mapping Spectrometry (IMS) is a compact snapshot hyperspectral imaging technology. However, the image mapper used in the IMS causes degradation of the reconstructed spectral datacube, such as, low spatial resolution, missing areas and stripe artifacts. In this paper, we propose an end-to-end deep learning method to jointly inpainting and super resolution the restored spectral images of the IMS. The method includes an image inpainting network, which is designed to correct the nonuniform intensity and missing data, and an image super resolution network, which aims to enhance the spatial resolution of images. In addition, a local nonuniformity correction method is proposed to preprocess the IMS images. Simulation and experimental results demonstrate the effectiveness of the proposed method.