Research map / Learning theory and algorithms / Matrix and tensor methods
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed two-stage algorithm instead of the compared methods | Image deblurring · Synthetic blurred images | kernel recovery error | Helped |
| Use the proposed two-stage algorithm instead of the compared methods | Image deblurring · Noisy blurred images | kernel recovery error | Helped |
| Use the proposed two-stage algorithm instead of the compared methods | Image deblurring · Real blurry images | kernel recovery error | Helped |
| Use the proposed two-stage algorithm instead of the compared methods | Image deblurring · Real blurry images | non-blind restoration error | No clear effect |
| Increase kernel size or activation-signal density | Proposed two-stage algorithm · Synthetic data | kernel recovery error | Hurt |
| Add Gaussian measurement noise | Proposed two-stage algorithm · Synthetic noisy data | kernel recovery error | Mixed |
Closest papers by meaning
- Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere (NeurIPS 2018 · similarity 0.93)
- Structured Local Minima in Sparse Blind Deconvolution (NeurIPS 2018 · similarity 0.92)
- A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution (NeurIPS 2019 · similarity 0.89)
- Geometry and Symmetry in Short-and-Sparse Deconvolution (ICML 2019 · similarity 0.88)
- Blind Deconvolutional Phase Retrieval via Convex Programming (NeurIPS 2018 · similarity 0.87)
- Blind Image Deconvolution by Automatic Gradient Activation (CVPR 2016 · similarity 0.87)
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