HyperSpectraNet: Leveraging Spectral Attention for Hyper-Spectral Image Reconstruction

In this paper, we present HyperSpectraNet, a specialized convolutional neural network model developed for the reconstruction of hyperspectral images (HSI). Containing rich spectral information, HSIs are widely used in fields such as environmental monitoring, agriculture, and medical imaging, offering detailed insights beyond the capabilities of standard imaging. The proposed model combines spectral and spatial attention mechanisms with Fourier transform interactions, addressing the complex demands of HSI reconstruction. This combination enhances the model's ability to identify and highlight detailed spectral features, which are essential for accurate HSI representation. We have evaluated the effectiveness of the model on the NTIRE 2022 hyperspectral dataset, where it provides considerable improvement in the image quality and accuracy of spectral details with 31.6 dB PSNR and 0.9442 SSIM. These results highlight the potential of the model in advancing HSI reconstruction technology.