Research map / Optimization / Optimizers
Sublabel-Accurate Relaxation of Nonconvex Energies
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use sublabel-accurate convexification instead of the baseline functional lifting relaxation | ROF model · ROF | energy | Helped |
| Use sublabel-accurate relaxation instead of baseline functional lifting | Robust truncated quadratic denoising · Gaussian and salt-and-pepper noise | energy | Helped |
| Use sublabel-accurate relaxation rather than the baseline with the same number of labels | Stereo matching · Middlebury stereo benchmark | depth-map quality | Helped |
| Use sublabel-accurate relaxation rather than baseline functional lifting | Phase unwrapping · Phase unwrapping | unwrapping quality | Helped |
| Use sublabel-accurate relaxation rather than baseline functional lifting | Depth from focus · Depth from focus | depth-map smoothness | Helped |
| Use isotropic rather than anisotropic regularization | Sublabel-accurate relaxation · Computer vision problems | grid bias | Helped |
| Use sublabel-accurate relaxation with anisotropic regularization instead of the DC-Linear and DC-MRF models | Robust truncated denoising · Robust truncated denoising | energy | Mixed |
| Use a convex lower bound of the lifted energy | Depth-from-focus relaxation · Depth from focus | regularizer influence | Helped |
Closest papers by meaning
- Lifting Vectorial Variational Problems: A Natural Formulation Based on Geometric Measure Theory and Discrete Exterior Calculus (CVPR 2019 · similarity 0.87)
- DOPE: Distributed Optimization for Pairwise Energies (CVPR 2017 · similarity 0.85)
- DS*: Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems (CVPR 2018 · similarity 0.85)
- Geometric Multi-Model Fitting With a Convex Relaxation Algorithm (CVPR 2018 · similarity 0.84)
- Revisiting Frank-Wolfe for Structured Nonconvex Optimization (NeurIPS 2025 · similarity 0.83)
- Tilted Sharpness-Aware Minimization (ICML 2025 · similarity 0.83)
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