PortraitSR: Artist-Inspired Prior Learning for Progressive Face Super-Resolution

Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from low-resolution (LR) inputs. While recent methods have advanced this task through architectural innovations and generative modeling, but they often leads to semantically inconsistent structures and unrealistic textures, particularly under high magnification. To mitigate these limitations, we draw inspiration from the human artistic process of “structuring before detailing” and propose a progressive prior-guided restoration strategy. Specifically, we first introduce a Sketching Structure Prior (SSP) module that embeds global semantics and refines local geometry through implicit parsing guidance and explicit spatial modulation. Then, a Associative Texture Prior (ATP) module leverages a High-Quality Dictionary (HD) learned from high-quality reconstruction to guide fine-grained detail recovery. Finally, to unify structure and detail features, we design a Holistic Prior Fusion (HPF) module that adaptively integrates them within semantically consistent facial regions. Extensive evaluations on CelebA and Helen datasets demonstrate that our method achieves superior performance in both structural fidelity and texture realism compared to existing state-of-the-art approaches.