Papers › Hierarchical Regression Network for Spectral Reconstruction from RGB Images
Hierarchical Regression Network for Spectral Reconstruction from RGB Images
Yuzhi Zhao, Lai-Man Po, Qiong Yan, Wei Liu, Tingyu Lin
Capturing visual image with a hyperspectral camera has been successfully applied to many areas due to its narrow-band imaging technology. Hyperspectral reconstruction from RGB images denotes a reverse process of hyperspectral imaging by discovering an inverse response function. Current works mainly map RGB images directly to corresponding spectrum but do not consider context information explicitly. Moreover, the use of encoder-decoder pair in current algorithms leads to loss of information. To address these problems, we propose a 4-level Hierarchical Regression Network (HRNet) with PixelShuffle layer as inter-level interaction. Furthermore, we adopt a residual dense block to remove artifacts of real world RGB images and a residual global block to build attention mechanism for enlarging perceptive field. We evaluate proposed HRNet with other architectures and techniques by participating in NTIRE 2020 Challenge on Spectral Reconstruction from RGB Images. The HRNet is the winning method of track 2 - real world images and ranks 3rd on track 1 - clean images. Please visit the project web page https://github.com/zhaoyuzhi/Hierarchical-Regression-Network-for-Spectral-Reconstruction-from-RGB-Images to try our codes and pre-trained models.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Spectral Reconstruction | ARAD-1K | HRNet | MRAE | 0.3476 | #10 of 11 | Archive leaderboard | report |
| Spectral Reconstruction | ARAD-1K | HRNet | PSNR | 26.89 | #10 of 11 | Archive leaderboard | report |
| Spectral Reconstruction | ARAD-1K | HRNet | RMSE | 0.0550 | #10 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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