Research map / Learning theory and algorithms / Matrix and tensor methods
Estimating Sparse Signals With Smooth Support via Convex Programming and Block Sparsity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add overlapping block-sparsity regularization to D-VTV | D-VTV image denoising · Berkeley Segmentation Database (300 images) | average PSNR gain (dB): +0.25 dB | Helped |
| Use CoLaMP with smooth-support regularization instead of group lasso | Compressive recovery of walking2 frames · walking2 surveillance video, M/K = 3 | runtime: 215s -> 1510s | Helped |
| Use the smooth-sparsity prior in CoLaMP rather than FPC | Compressive recovery of walking2 frames · walking2 surveillance video | normalized reconstruction error | Helped |
| Use CoSaMP rather than FPC | Noisy compressive signal recovery · Shepp–Logan phantom, SNR 5–20 dB | reconstruction error: 1.5K fewer measurements | Helped |
| Increase CoLaMP regularization over iterations (ablation) | CoLaMP noisy signal recovery · Shepp–Logan phantom | perceived reconstruction quality | Helped |
| Use 8-connected pixel cliques instead of 2×2 cliques (ablation) | CoLaMP · Shepp–Logan phantom | perceived closeness to target signal | Mixed |
| Use fast-convolution FBS instead of ADMM | RPCA with 10×10 cliques · Airport surveillance video | memory requirement: 204NL vs. 4NL | Helped |
| Use 3×3 overlapping cliques rather than 10×10 cliques (ablation) | Structured-sparsity RPCA · Airport surveillance video | foreground-background separation | No clear effect |
Closest papers by meaning
- A convex program for bilinear inversion of sparse vectors (NeurIPS 2018 · similarity 0.89)
- Sparse Support Recovery with Non-smooth Loss Functions (NeurIPS 2016 · similarity 0.89)
- Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery (AAAI 2024 · similarity 0.88)
- The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements (NeurIPS 2025 · similarity 0.88)
- Straight-Through Meets Sparse Recovery: the Support Exploration Algorithm (ICML 2024 · similarity 0.86)
- Sketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees (ICLR 2025 · similarity 0.85)
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