Spectral imaging with deep learning
The goal of spectral imaging is to capture the spectral signature of a target. Traditional
scanning method for spectral imaging suffers from large system volume and low image …
scanning method for spectral imaging suffers from large system volume and low image …
Ntire 2022 spectral recovery challenge and data set
This paper reviews the third biennial challenge on spectral reconstruction from RGB images,
ie, the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB …
ie, the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB …
Mst++: Multi-stage spectral-wise transformer for efficient spectral reconstruction
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or
wider convolutional neural networks (CNNs) to learn the end-to-end map** from the RGB …
wider convolutional neural networks (CNNs) to learn the end-to-end map** from the RGB …
ESSAformer: Efficient transformer for hyperspectral image super-resolution
Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-
resolution hyperspectral image from a low-resolution observation. However, the prevailing …
resolution hyperspectral image from a low-resolution observation. However, the prevailing …
Spectral super-resolution meets deep learning: Achievements and challenges
Ntire 2020 challenge on real-world image super-resolution: Methods and results
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on
the participating methods and final results. The challenge addresses the real world setting …
the participating methods and final results. The challenge addresses the real world setting …
Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution
Due to the limitations of hyperspectral imaging systems, hyperspectral imagery (HSI) often
suffers from poor spatial resolution, thus hampering many applications of the imagery …
suffers from poor spatial resolution, thus hampering many applications of the imagery …
Hscnn+: Advanced cnn-based hyperspectral recovery from rgb images
Hyperspectral recovery from a single RGB image has seen a great improvement with the
development of deep convolutional neural networks (CNNs). In this paper, we propose two …
development of deep convolutional neural networks (CNNs). In this paper, we propose two …