Implicit Subspace Prior Learning, or ISPL, is a framework to approach dual-blind face restoration, with two major distinctions from previous restoration methods: 1) Instead of assuming an explicit degradation function between LQ and HQ domain, it establishes an implicit correspondence between both domains via a mutual embedding space, thus avoid solving the pathological inverse problem directly. 2) A subspace prior decomposition and fusion mechanism to dynamically handle inputs at varying degradation levels with consistent high-quality restoration results.
Source: Implicit Subspace Prior Learning for Dual-Blind Face RestorationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Few-Shot Learning | 2 | 40.00% |
Meta-Learning | 2 | 40.00% |
Blind Face Restoration | 1 | 20.00% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |