Super-resolution computed tomography imaging spectrometry

Computed tomography imaging spectrometry (CTIS) is a snapshot spectral imaging technique that relies on a limited number of projections of the target data cube (2D spatial and 1D spectral), which can be reconstructed via a delicate tomographic reconstruction algorithm. However, the restricted angle difference between the projections and the space division multiplexing of the projections make the reconstruction suffer from severe artifacts as well as a low spatial resolution. In this paper, we demonstrate super-resolution computed tomography imaging spec-trometry (SRCTIS) by assimilating the information obtained by a conventional CTIS system and a regular RGB camera, which has a higher pixel resolution. To improve the reconstruction accuracy of CTIS, the unique in-formation provided by the zero-order diffraction of the target scene is used as a guidance image for filtering to better preserve the edges and reduce artifacts. The recovered multispectral image is then mapped onto the RGB image according to camera calibration. Finally, based on the spectral and the spatial continuities of the target scene, the multispectral information obtained from CTIS is propagated to each pixel of the RGB image to enhance its spectral resolution, resulting in SRCTIS. Both stimulative studies and proof-of-concept experi-ments were then conducted, and the results quantified by key metrics, such as structural similarity index mea-surement and spectral angle mapping have suggested that the developed method cannot only suppress the reconstruction artifacts, but also simultaneously achieve high spatial and spectral resolutions. (c) 2023 Chinese Laser Press

Automated summary

This paper presents a super-resolution computed tomography imaging spectrometry (SRCTIS) technique by integrating the information obtained by a conventional CTIS system and a regular RGB camera, which improves the reconstruction accuracy and spatial resolution of CTIS. The unique information provided by the zero-order diffraction of the target scene is used for filtering to preserve edges and reduce artifacts. The multispectral image recovered is then mapped onto the RGB image and the spectral information is propagated to enhance the spectral resolution, achieving high spatial and spectral resolutions.