Research map / Image generation / Image restoration
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase number of unrolled iterations per layer | T3SC · ICVL sigma=50 | PSNR / inference time | Mixed |
| two-layer model instead of single-layer models (ablation) | T3SC · ICVL sigma=50 | MPSNR | Helped |
| add band-wise noise estimator with weights beta_j (ablation) | T3SC · ICVL band-dependent Gaussian and stripes noise | MPSNR | Helped |
| self-supervised training instead of supervised | T3SC-SSL · ICVL complex noise | MPSNR | Hurt |
| share second layer across APEX, DC Mall, Pavia in joint training | T3SC · APEX/DCMall/Pavia | MPSNR | No clear effect |
Closest papers by meaning
- Exploiting Spectral-Spatial Correlation for Coded Hyperspectral Image Restoration (CVPR 2016 · similarity 0.90)
- HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models (CVPR 2024 · similarity 0.89)
- Hyperspectral Image Reconstruction Using a Deep Spatial-Spectral Prior (CVPR 2019 · similarity 0.89)
- LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift (NeurIPS 2026 · similarity 0.87)
- A Selective Re-learning Mechanism for Hyperspectral Fusion Imaging (CVPR 2025 · similarity 0.86)
- Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising (CVPR 2024 · similarity 0.86)
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