ISPL

2000년 도입 · 논문 3편에서 사용

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.

출처: Implicit Subspace Prior Learning for Dual-Blind Face Restoration

소개 논문: Implicit Subspace Prior Learning for Dual-Blind Face Restoration