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
Enhanced Deep Residual Networks for Single Image Super-Resolution
LimBee/NTIRE2017
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Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN).
Learning Enriched Features for Real Image Restoration and Enhancement
With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing.
Restormer: Efficient Transformer for High-Resolution Image Restoration
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Multi-Stage Progressive Image Restoration
At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
caiyuanhao1998/MST
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CVPR 2022
The HSI representations are highly similar and correlated across the spectral dimension.
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 mapping from the RGB image to its hyperspectral image (HSI).
StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization
In recent years, neural vocoders have surpassed classical speech generation approaches in naturalness and perceptual quality of the synthesized speech.
HINet: Half Instance Normalization Network for Image Restoration
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks.
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
caiyuanhao1998/MST
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CVPR 2022
On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features.
An efficient CNN for spectral reconstruction from RGB images
Recently, the example-based single image spectral reconstruction from RGB images task, aka, spectral super-resolution was approached by means of deep learning by Galliani et al.
