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HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly learn an optimized binary mask with reconstruction (ablation) | HerosNet · KAIST | PSNR: 33.15 -> 34.45; 1.30dB | Helped |
| Add hierarchical feature interaction between phases instead of a U-Net prior without interaction (ablation) | HerosNet · KAIST | PSNR: 33.41 -> 34.45; 1.04dB | Helped |
| Add the residual module (ablation) | HerosNet · KAIST | PSNR: 33.59 -> 34.45 | Helped |
| Use a dynamic step size rather than the static component alone (ablation) | HerosNet · KAIST | PSNR: 34.11 -> 34.45; 0.34dB | Helped |
| Use an optimized binary mask rather than a fixed real mask (ablation) | HerosNet · KAIST | SSIM: 0.960 -> 0.970; 0.010 | Helped |
Closest papers by meaning
- Detail Matters: Mamba-Inspired Joint Unfolding Network for Snapshot Spectral Compressive Imaging (AAAI 2025 · similarity 0.89)
- Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms (NeurIPS 2024 · similarity 0.89)
- Spectral Compressive Imaging via Chromaticity-Intensity Decomposition (NeurIPS 2025 · similarity 0.87)
- FRN: Fractal-Based Recursive Spectral Reconstruction Network (NeurIPS 2025 · similarity 0.87)
- Dual-Window Multiscale Transformer for Hyperspectral Snapshot Compressive Imaging (AAAI 2024 · similarity 0.87)
- Sp3ctralMamba: Physics-Driven Joint State Space Model for Hyperspectral Image Reconstruction (AAAI 2025 · similarity 0.87)
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