Face restoration is an inherently ill-posed problem, where additional prior
constraints are typically considered crucial for mitigating such pathology.
However, real-world image prior are often hard to simulate with precise
mathematical models, which inevitably limits the performance and generalization
ability of existing prior-regularized restoration methods. In this paper, we
study the problem of face restoration under a more practical ``dual blind''
setting, i.e., without prior assumptions or hand-crafted regularization terms
on the degradation profile or image contents.
To this end, a novel implicit subspace prior learning (ISPL) framework is
proposed as a generic solution to dual-blind face restoration, with two key
elements: 1) an implicit formulation to circumvent the ill-defined restoration
mapping and 2) a subspace prior decomposition and fusion mechanism to
dynamically handle inputs at varying degradation levels with consistent
high-quality restoration results.
Experimental results demonstrate significant perception-distortion
improvement of ISPL against existing state-of-the-art methods for a variety of
restoration subtasks, including a 3.69db PSNR and 45.8% FID gain against
ESRGAN, the 2018 NTIRE SR challenge winner. Overall, we prove that it is
possible to capture and utilize prior knowledge without explicitly formulating
it, which will help inspire new research paradigms towards low-level vision
Lingbo Yang, Pan Wang, Zhanning Gao, Shanshe Wang, Peiran Ren, Siwei Ma, Wen Gao