Spectral super-resolution (sSR) is a very important technique to obtain hyperspectral images from only RGB images, which can effectively overcome the high acquisition cost and low …
Since their inception in the 1930–1960s, the research disciplines of computational imaging and machine learning have followed parallel tracks and, during the last two decades …
M Yako, Y Yamaoka, T Kiyohara, C Hosokawa… - Nature …, 2023 - nature.com
Hyperspectral (HS) imaging provides rich spatial and spectral information and extends image inspection beyond human perception. Existing approaches, however, suffer from …
Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) restoration, which includes the tasks of denoising …
A Romero, C Gatta… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi-and hyperspectral imagery of supervised …
X Miao, X Yuan, Y Pu… - Proceedings of the IEEE …, 2019 - openaccess.thecvf.com
We propose the l-net, which reconstructs hyperspectral images (eg, with 24 spectral channels) from a single shot measurement. This task is usually termed snapshot …
Hyperspectral imaging is an essential imaging modality for a wide range of applications, especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral …
We propose a plug-and-play (PnP) method that uses deep-learning-based denoisers as regularization priors for spectral snapshot compressive imaging (SCI). Our method is …
L Wang, C Sun, Y Fu, MH Kim… - Proceedings of the …, 2019 - openaccess.thecvf.com
Regularization is a fundamental technique to solve an ill-posed optimization problem robustly and is essential to reconstruct compressive hyperspectral images. Various hand …