Research map / Optimization / Nonconvex and smooth optimization
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Huber loss instead of the ℓ4 loss | Two-stage MCS-BD optimization · Synthetic MCS-BD, varying sparsity | recovery probability | Helped |
| Use Huber loss instead of the ℓ4 loss | Two-stage MCS-BD optimization · Synthetic MCS-BD, phase transition on (p,n) | recovery probability | Helped |
| Use Huber loss instead of the ℓ4 loss | Two-stage MCS-BD optimization · Tubulin Conj-AL647 STORM imaging | PSF recovery | Helped |
| Use ℓ1 loss instead of the ℓ4 loss | MCS-BD optimization · Synthetic MCS-BD | recovery probability | Helped |
| Use ℓ1 loss instead of Huber loss | MCS-BD optimization · Synthetic MCS-BD | recovery accuracy | Helped |
| Add LP rounding after Huber-loss RGD (ablation) | Huber-loss MCS-BD · Synthetic MCS-BD | recovery exactness | Helped |
| Add LP rounding after ℓ4-loss RGD (ablation) | ℓ4-loss MCS-BD · Synthetic MCS-BD | recovery exactness | Helped |
| Remove LP rounding and use Riemannian subgradient descent on ℓ1 loss (ablation) | ℓ1-loss MCS-BD · Synthetic MCS-BD | recovery exactness | No clear effect |
Closest papers by meaning
- Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere (NeurIPS 2018 · similarity 0.92)
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