Blind Face Restoration
24 papers with code • 4 benchmarks • 4 datasets
Blind face restoration aims at recovering high-quality faces from the low-quality counterparts suffering from unknown degradation, such as low-resolution, noise, blur, compression artifacts, etc. When applied to real-world scenarios, it becomes more challenging, due to more complicated degradation, diverse poses and expressions.
Description source: Towards Real-World Blind Face Restoration with Generative Facial Prior
Image source: Towards Real-World Blind Face Restoration with Generative Facial Prior
Libraries
Use these libraries to find Blind Face Restoration models and implementations
CelebA
CelebA-HQ
LFW
WIDER
Most implemented papers
RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs
wzhouxiff/restoreformerplusplus
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In this work, we propose RestoreFormer++, which on the one hand introduces fully-spatial attention mechanisms to model the contextual information and the interplay with the priors, and on the other hand, explores an extending degrading model to help generate more realistic degraded face images to alleviate the synthetic-to-real-world gap.
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework.
Towards Real-World Blind Face Restoration with Generative Diffusion Prior
We propose BFRffusion which is thoughtfully designed to effectively extract features from low-quality face images and could restore realistic and faithful facial details with the generative prior of the pretrained Stable Diffusion.
Efficient Diffusion Model for Image Restoration by Residual Shifting
zsyoaoa/resshift
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While diffusion-based image restoration (IR) methods have achieved remarkable success, they are still limited by the low inference speed attributed to the necessity of executing hundreds or even thousands of sampling steps.
