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
Latest papers
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.
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.
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.
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.
Dual Associated Encoder for Face Restoration
LIAGM/DAEFR
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Restoring facial details from low-quality (LQ) images has remained a challenging problem due to its ill-posedness induced by various degradations in the wild.
OPHAvatars: One-shot Photo-realistic Head Avatars
And with the coarse video, our method synthesizes a coarse talking head avatar with a deforming neural radiance field.
DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration
Kaldwin0106/DR2_Drgradation_Remover
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CVPR 2023
However, it is expensive and infeasible to include every type of degradation to cover real-world cases in the training data.
DifFace: Blind Face Restoration with Diffused Error Contraction
zsyoaoa/difface
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Moreover, the transition distribution can contract the error of the restoration backbone and thus makes our method more robust to unknown degradations.
Learning Dual Memory Dictionaries for Blind Face Restoration
Generally, it is a challenging and intractable task to improve the photo-realistic performance of blind restoration and adaptively handle the generic and specific restoration scenarios with a single unified model.
Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration
yyj1ang/mfpsnet
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To this end, we propose a Face Restoration Searching Network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality.
