Spectral Super-Resolution

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Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (18 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
  17 Apr 2022 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  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).
• Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-Resolution
  30 May 2025 1 repository listed
  For image fusion, we integrate low-frequency estimation and high-frequency injection modules to combine the spatial and spectral information from spatial super-resolution and spectral super-resolution outputs.
• Frequency Estimation Using Complex-Valued Shifted Window Transformer
  17 Sep 2023 1 repository listed
  In this letter, we introduce 1-D real-valued and complex-valued shifted window (Swin) transformers, referred to as SwinFreq and CVSwinFreq, respectively, for line-spectra frequency estimation on 1-D complex-valued…
• HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution
  29 Dec 2021 1 repository listed
  Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart.
• An efficient CNN for spectral reconstruction from RGB images
  12 Apr 2018 1 repository listed
  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.
• Learned Spectral Super-Resolution
  28 Mar 2017 1 repository listed
  We describe a novel method for blind, single-image spectral super-resolution.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.