ISPL
Implicit Subspace Prior Learning
**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.
Implicit Subspace Prior Learning for Dual-Blind Face Restoration
https://paperswithcode.com/paper/implicit-subspace-prior-learning-for-dual
Implicit Subspace Prior Learning for Dual-Blind Face Restoration