Self-supervised constrained super-resolution fast coded spectral imaging system

Current coded aperture spectral imaging techniques suffer from slow imaging speeds and poor image quality at low sampling rates. To address these issues, we propose a self-supervised constrained super-resolution fast coded spectral imaging system by combining a physical process of spectral imaging with image fusion super-resolution techniques. We constructed a discrete cosine transform spectrometer (DCTS) and successfully acquired low-resolution hyperspectral images (LR-HSI) and high-resolution multispectral images (HR-MSI). In order to enhance the resolution of hyperspectral images, we designed a self-supervised spectral image super-resolution network based on the physical process of spectral imaging: SSAM-Unet. This network successfully reconstructs the HR-HSI by fusing the LR-HSI with the HR-MSI. We verified the performance and generalization ability of this method through extensive experiments. Experimental results show that our proposed method can achieve good imaging results at very low sampling rates.