Spectral Reconstruction
29 papers with code • 4 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Spectral Reconstruction
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Libraries
Use these libraries to find Spectral Reconstruction models and implementations
CAVE
Most implemented papers
Light Weight Residual Dense Attention Net for Spectral Reconstruction from RGB Images
sabaridsn/LightWeightModel
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Hyperspectral Imaging is the acquisition of spectral and spatial information of a particular scene.
NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image
caiyuanhao1998/MST-plus-plus
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This paper reviews the second challenge on spectral reconstruction from RGB images, i. e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image.
Hierarchical Regression Network for Spectral Reconstruction from RGB Images
zhaoyuzhi/Hierarchical-Regression-Network-for-Spectral-Reconstruction-from-RGB-Images
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Hyperspectral reconstruction from RGB images denotes a reverse process of hyperspectral imaging by discovering an inverse response function.
AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images
Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs).
Spectral Synthesis for Satellite-to-Satellite Translation
anonymous-ai-for-earth/satellite-to-satellite-translation
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These satellites have different vantage points above the earth and different spectral imaging bands resulting in inconsistent imagery from one to another.
Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning
In this application, the spectrally coded light field camera can be interpreted as a single-shot spectral depth camera.
Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio Sequences
darshandeshpande/audio-spectral-enhancement
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With active research in audio compression techniques yielding substantial breakthroughs, spectral reconstruction of low-quality audio waves remains a less indulged topic.
Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images
zbzhzhy/gd-net
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Then, we design a lightweight neural network with a multi-stage architecture to mimic the formed amended gradient descent process, in which efficient convolution and novel spectral zero-mean normalization are proposed to effectively extract spatial-spectral features for regressing an initialization, a basic gradient, and an incremental gradient.
Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild
zbzhzhy/unsupervised-spectral-reconstruction
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ICCV 2021
Specifically, on the basis of the intrinsic imaging degradation model of RGB images from HS images, we progressively spread the differences between input RGB images and re-projected RGB images from recovered HS images via effective unsupervised camera spectral response function estimation.
Deep Low-Dimensional Spectral Image Representation for Compressive Spectral Reconstruction
hdspgroup/DeepLDSIR
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IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP) 2021
This paper proposes an autoencoder-based network that guarantees a low-dimensional spectral representation through feature reduction, which can be used as prior in the compressive spectral imaging reconstruction.
