Face-Hallucination-Benchmark

A list of face face super-resolution/hallucination resources collected by Junjun Jiang and Chenyang Wang. If you find these resources are useful, please cite our following survey paper.

Survey paper

J. Jiang, C. Wang, X. Liu, and J. Ma, “Deep Learning-based Face Super-resolution: A Survey,” ACM Computing Surveys, vol. 55, no. 1, pp. 1-36, 2023. [pdf]

@article{jiang2021survey
  title={Deep Learning-based Face Super-resolution: A Survey},
  author={Jiang, Junjun and Wang, Chenyang and Liu, Xianming and Ma, Jiayi},
  journal={ACM Computing Surveys},
  volume={55},
  number={1},
  pages={1-36},
  year={2023}
}

*Some classical algorithms (including NE, LSR, SR, LcR, LINE, TLcR-RL, and EigTran) implemented by myself can be found here.

*As for deep learning-based methods, we provide the training sets [Baidu Drive]（33k5）, and the experimental results of several state-of-the-art methods in [Baidu Drive](va2i) and [Google Drive]. Note that the partition of the dataset follows [DIC]. The eval_psnr_ssim.py and calc_lpips.py are built on [DIC] and [LPIPS]. We thank the authors for sharing their codes.

Deep learning-based Methods

Diffusion Based Methods

• [DR2] DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration, Z. Wang et al., CVPR 2023. [PDF]

• [PGDiff] PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance, P. Yang et al., NIPS 2023 [PDF]

• Implicit Diffusion Models for Continuous Super-Resolution, S. Gao et al., CVPR 2023. [PDF]

• DifFace: Blind Face Restoration with Diffused Error Contraction, Z. Yue et al., ARXIV 2023. [PDF]

• Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond, Y. Zhao et al., ICCV 2023. [PDF]

• Towards Real-World Blind Face Restoration with Generative Diffusion Prior, X. Chen et al. ARXIV 2023. [PDF]

General FSR Methods

• [BCCNN] Learning Face Hallucination in the Wild, E. Zhou et al., AAAI 2015.

• [URDGN] Ultra-resolving face images by discriminative generative networks, X. Yu et al., CVPR 2016. [Web]

• [SRCNN-IBP] Face Hallucination Using Convolutional Neural Network with Iterative Back Projection, D. Huang et al., CCBR 2016.

• [GLN] Global-Local Face Upsampling Network, O. Tuzel et al., ArXiv 2016.

• [GLFSR] Global-local fusion network for face super-resolution, Tao Lu et al., Neurocomputing 2020.

• Patch-based face hallucination with multitask deep neural network, W. Ko et al., ICME 2016.

• Face hallucination by deep traversal network, Z. Feng et at., ICPR 2016.

• Face hallucination using region-based deep convolutional networks, T. Lu et al., ICIP 2017.

• Face Super-Resolution Through Wasserstein GANs. Z. Chen et al., ArXiv 2017.

• High-Quality Face Image SR Using Conditional Generative Adversarial Networks, B. Huang et al., ArXiv 2017.

• [WaSRNet] Wavelet-SRNet: A Wavelet-Based CNN for Multi-Scale Face Super Resolution, H. Huang et al., ICCV 2017.

• [Attention-FH] Attention-Aware Face Hallucination via Deep Reinforcement Learning, Q. Cao et al., CVPR 2017. [PDF][Web]

• Super-resolution Reconstruction of Face Image Based on Convolution Network, W. Huang et al., AISC 2018.

• A Noise Robust Face Hallucination Framework Via Cascaded Model of Deep Convolutional Networks and Manifold Learning, L. Han et al., ICME 2018

• Face Hallucination via Convolution Neural Network, H. Nie et al., ICTAI 2018.

• Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning, Yukai Shi et al. TPAMI 2019.

• Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement, Yibing Song et al. IJCV 2019. [Web]

• Sequential Gating Ensemble Network for Noise Robust Multiscale Face Restoration, Z. chen et al., TCYB 2019.

• Face Image Super-Resolution Using Inception Residual Network and GAN Framework, S. D. Indradi et al., ICOICT 2019.

• Guided Cyclegan Via Semi-Dual Optimal Transport for Photo-Realistic Face Super-Resolution, W. Zheng et al., ICIP 2019.

• ATMFN: Adaptive-threshold-based Multi-model Fusion Network for Compressed Face Hallucination, K. Jiang et al., TMM 2019.

• [SRDSI] Face hallucination from low quality images using definition-scalable inference, X. Hu et al. PR 2019.

• RBPNET: An asymptotic Residual Back-Projection Network for super-resolution of very low-resolution face image, X. Wang et al., Neurocomputing 2020.

• Efficient Face Super-Resolution Based on Separable Convolution Projection Networks, X. Chen et al., CRC 2020.

• A Densely Connected Face Super-Resolution Network Based on Attention Mechanism, Y. Liu et al., ICIEA 2020.

• [HiFaceGAN] Implicit Subspace Prior Learning for Dual-Blind Face Restoration, L. Yang et al., ArXiv 2020.

• Super-resolving Tiny Faces with Face Feature Vectors, Y. Lu et al., ICIST 2020.

• [SPARNet]Learning Spatial Attention for Face Super-Resolution, C. Chen et al., TIP 2020. [Web]

• PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution, H. Du et al., ACM MM 2020.

• [SPGAN] Supervised Pixel-Wise GAN for Face Super-Resolution, M. Zhang et al., TMM 2020.

• Robust Super-Resolution of Real Faces using Smooth Features, S. Goswami et al., ECCVW 2020.

• Learning wavelet coefficients for face super-resolution, Y. Liu et al., VC 2020.

• PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models, S. Memon et al,. CVPR 2020.

• Characteristic Regularisation for Super-Resolving Face Images, Z. Cheng et al., WACV 2020.

• [DPDFN] Dual-path deep fusion network for face image hallucination, K. Jiang, TMM 2020.

• GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution, K. C. K. Chan et al., CVPR 2021.

• GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond, K. C. K. Chan et al., TPAMI 2022 [PDF]

• [GFP-GAN] Towards Real-World Blind Face Restoration with Generative Facial Prior, X. Wang et al., CVPR 2021.

• [GPEN] GAN Prior Embedded Network for Blind Face Restoration in the Wild, T. Yang et al., CVPR 2021.

• Generative Facial Prior for Large-Factor Blind Face Super-Resolution, X Gua et al., ICAITA 2021. [PDF]

• E-ComSupResNet: Enhanced Face Super-Resolution Through Compact Network, E. Chudasama et al., TBIOM 2021.

• [MLGE] Multi-Laplacian GAN with Edge Enhancement for Face Super Resolution, S. Ko et al., ICPR 2021.

• [TANet] TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network, Z. Wang et al., ArXiv 2021.[PDF]

• Face Hallucination via Split-Attention in Split-Attention Network, T. Lu et al., ACMMM 2021. [Web]

• [SelFSR] SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network, X. Zeng et al., ArXiv 2021. [PDF]

• [FRGAN] FRGAN: A Blind Face Restoration with Generative Adversarial Networks, T. Wei et al., MPE 2021 [PDF]

• [Panini-Net] Panini-Net: GAN Prior based Degradation-Aware Feature Interpolation for Face Restoration, Y. Wang et al., AAAI 2022.

• [GCFSR] GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors, J. He et al., CVPR2022 [PDF]

• Blind Face Restoration: Benchmark Datasets and a Baseline Model, P. Zhang et al., ArXiv 2022. [Web] [PDF]

• [EDFace-Celeb-1M] EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset, K. Zhang et al., TPAMI 2022 [Web] [PDF]

Prior-guided FSR Methods

• [CBN] Deep cascaded bi-network for face hallucination, S. Zhu et al., ECCV 2016. [PDF][Web]

• [KPEFH] Face Hallucination Based on Key Parts Enhancement, K. Li et al., ICASSP 2018.

• [LCGE] Learning to hallucinate face images via component generation and enhancement, Y. Song et al., IJCAI 2017 [PDF][Web]

• [MNCEFH] Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination, J. Jiang et al., IJCAI 2018. [PDF][Web]

• [FSRNet] FSRNet: End-to-End learning face super-resolution with facial priors, Y. Chen et al., CVPR 2018. [PDF][Web]

• [FSRGFCH] Face super-resolution guided by facial component heatmaps, ECCV 2018, X. Yu et al. [PDF] [Web]

• A coarse-to-fine face hallucination method by exploiting facial prior knowledge, ICIP 2018, Mengyan Li et al. [PDF][Web]

• [PFSRNet] Progressive Face Super-Resolution via Attention to Facial Landmark, D. Kim et al., BMVC 2019. [PDF][Code]

• [JASRNet] Joint Super-Resolution and Alignment of Tiny Faces, Y. Yin et al. AAAI 2019.

• Component Attention Guided Face Super-Resolution Network: CAGFace, R. Kalarot et al., WACV 2020.

• SAAN: Semantic Attention Adaptation Network for Face Super-Resolution, T. Zhao et al., ICME 2020.

• [PMGMSAN] Parsing Map Guided Multi-Scale Attention Network For Face Hallucination, C. Wang et al., ICASSP 2020.

• [ATSENet] Learning Face Image Super-Resolution through Facial Semantic Attribute Transformation and Self-Attentive Structure Enhancement, M. Li et al., TMM 2020.

• [DIC] Deep Face Super-Resolution With Iterative Collaboration Between Attentive Recovery and Landmark Estimation, Cheng Ma et al., CVPR 2020.

• MSFSR: A Multi-Stage Face Super-Resolution with Accurate Facial Representation via Enhanced Facial Boundaries, Y. Zhang et al., CVPRW 2020.

• Semantic-driven Face Hallucination Based on Residual Network, X. Yu et al., TBIOM 2021

• Progressive Semantic-Aware Style Transformation for Blind Face Restoration, C. Chen et al., CVPR 2021

• [HapFSR] Heatmap-Aware Pyramid Face Hallucination, C. Wang et al. ICME 2021.

• [OBC-FSR] Organ-Branched CNN for Robust Face Super-Resolution, J. Li et al., ICME 2021.

• [HCRF] Features Guided Face Super-Resolution via Hybrid Model of Deep Learning and Random Forests, Z. S. Liu et al., TIP 2021.

• DCLNet: Dual Closed-loop Networks for face super-resolution, H. Wang et al., KBS 2021.

• Progressive face super-resolution with cascaded recurrent convolutional network, S. Liu et al., Neurocomputing 2021.

• Face Super-Resolution Network with Incremental Enhancement of Facial Parsing Information, S. Liu et al., ICPR 2021.

• Unsupervised face super-resolution via gradient enhancement and semantic guidance, L. Li et al., VC 2021.

• SemFSR: An Unsupervised Face SR with Semantic Features for Multiple Degradations, H. Qi et al., ICTAI 2021.

• Face Restoration via Plug-and-Play 3D Facial Priors, X. Hu et al., TPAMI 2021, [PDF]

• Blind Face Restoration via Multi-Prior Collaboration and Adaptive Feature Fusion, Z. Teng et al., FNBOT 2022 [Web]

• Blind Face Restoration via Integrating Face Shape and Generative Priors, F. Zhu et al., CVPR 2022.

• Propagating Facial Prior Knowledge for Multi-Task Learning in Face Super-Resolution, C. Wang et al., TCSVT 2022. [Code] [Our Work]

• Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration, Y. Yu et al., ArXiv 2022. [Web] [PDF]

Attribute-constrained FSR Methods

• [FaceAttr] Super-resolving very low-resolution face images with supplementary attributes, CVPR2018, Xin Yu et al. [PDF][Web]

• Attribute-Guided Face Generation Using Conditional CycleGAN, ECCV2018, Yongyi Lu et al. [PDF][Web]

• Attribute Augmented Convolutional Neural Network for Face Hallucination, CVPRW2018, Cheng-Han Lee et al. [PDF][Web]

• Residual Attribute Attention Network for Face Image Super-Resolution, Jingwei Xin et al. AAAI2019. [PDF]

• [ATNet] Deep Learning Face Hallucination via Attributes Transfer and Enhancement, M. Li et al., ICME 2019.

• [FACN] Facial Attribute Capsules for Noise Face Super Resolution, J. Xin et al., AAAI 2020.

• [ATSENet] Learning Face Image Super-Resolution through Facial Semantic Attribute Transformation and Self-Attentive Structure Enhancement, M. Li et al., TMM 2020.

• [AGA-GAN] AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination, A. Srivastava et al. ArXiv 2021. [PDF]

Idnetity-preserving FSR Methods

• [SICNN] Super-Identity Convolutional Neural Network for Face Hallucination, K. Zhang et al., ECCV 2018. [PDF][Web]

• [FH-GAN] FH-GAN: Face Hallucination and Recognition Using Generative Adversarial Network, B. Bayramli et al., NIP 2019.

• [WaSRGAN] Wavelet domain generative adversarial network for multi-scale face hallucination, H. Huang et al., IJCV 2019. [Code]

• Low-Resolution Face Recognition Based on Identity-Preserved Face Hallucination, S. Lai et al., ICIP 2019.

• [IPFH] Identity-Preserving Face Hallucination via Deep Reinforcement Learning, X. Cheng et al., TCSVT 2019.

• Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-resolution Network, E. Ataer-Cansizoglu et al., ArXiv 2019.

• Optimizing Super Resolution for Face Recognition, A. A. Abello et al., SIBGRAPI 2019.

• SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination, C.Hsu et al., TIP 2019. [Code]

• [IADFH] Identity-Aware Deep Face Hallucination via Adversarial Face Verification, H. Kazemi et al., BTAS 2019.

• [C-SRIP] Face Hallucination Using Cascaded Super-Resolution and Identity Priors, K. Grm et al., TIP 2020.

• [SPGAN] Supervised Pixel-Wise GAN for Face Super-Resolution, M. Zhang et al., TMM 2020.

• Identity-Aware Face Super-Resolution for Low-Resolution Face Recognition, J. Chen et al., SPL 2020.

• Face Super-Resolution Through Dual-Identity Constraint, F. Cheng et al., ICME 2021.

• Edge and identity preserving network for face super-resolution, J. Kim et al., Neurocomputing 2021.

• Super-resolution of very low-resolution face images with a wavelet integrated, identity preserving, adversarial network， H. Dastmalchi, et al., Signal Processing: Image Communication 2022. [Code]

• Distilling Resolution-robust Identity Knowledge for Texture-Enhanced Face Hallucination, Q, Bao, et al., ACM MM 2022. [PDF]

Reference FSR Methods

• [GFRNet] Learning Warped Guidance for Blind Face Restoration, X. Li et al., ECCV 2019.

• [GWAInet] Exemplar Guided Face Image Super-Resolution without Facial Landmarks, CVPRW 2019.

• [JSRFC] Recovering Extremely Degraded Faces by Joint Super-Resolution and Facial Composite, X. Li et al., ICTAI 2019.

• [ASFFNet] Enhanced Blind Face Restoration With Multi-Exemplar Images and Adaptive Spatial Feature Fusion, X. Li et al., CVPR 2020.[Web]

• [MEFSR] Multiple Exemplars-based Hallucination for Face Super-resolution and Editing, K. Wang et al., ACCV 2020.

• [DFDNet] Blind Face Restoration via Deep Multi-scale Component Dictionaries, X. Li et al. ECCV 2020. [Web]

• Gluing Reference Patches Together for Face Super-Resolution, J. Kim et al. IEEE Access 2021. [pdf]

• Semantic-Aware Latent Space Exploration for Face Image Restoration, Y. Guo, et al., ICME 2022. [PDF]

• [RestoreFormer] RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs, Z. Wang et al., CVPR 2022 [PDF]

• [RestoreFormer++] RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs, Z. Wang et al., TPAMI 2023 [PDF]

• Rethinking Deep Face Restoration, Y. Zhao et al., CVPR 2022 [PDF]

• [VQFR] VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder，Y. Gu et al., ECCV 2022. [PDF]

• Towards Robust Blind Face Restoration with Codebook Lookup Transforme, S. Zhou et al., NIPS 2022. [PDF]

• Learning Dual Memory Dictionaries for Blind Face Restoration，X. Li, et al., TPAMI 2022. [PDF]

Real-World FSR Methods

• [LRGAN] To learn image super-resolution, use a GAN to learn how to do image degradation first, A.Bulat et al., ECCV 2018. [PDF][Web]

• Super-FAN: integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs A. Bulat et al., CVPR 2018. [PDF][Web]

• Real-World Super-Resolution of Face-Images from Surveillance Cameras, A. Aakerberg et al., ArXiv 2021.

• [SCGAN] Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution, H. Hou et al., TIP 2023. [PDF][Code]

Classical Methods

Classical Patch-based Methods

• Hallucinating face, S. Baker and T. Kanade, FG 2000. [PDF]

• [NE] Super-resolution through neighbor embedding, Chang et al. CVPR 2004. [Web]

• [LSR] Hallucinating face by position-patch, Ma et al., PR 2010. [Web]

• [SR] Position-patch based face hallucination using convex optimization, C. Jung et al., SPL 2010. [Web]

• [LcR] Noise robust face hallucination via locality-constrained representation, J. Jiang et al., TMM 2014.[Web]

• [LINE] Multilayer Locality-Constrained Iterative Neighbor Embedding, J. Jiang et al., TIP 2014. [Web]

• Face Hallucination Using Linear Models of Coupled Sparse Support, R. A. Farrugia et al., TIP 2017.[PDF][Web]

• Hallucinating Face Image by Regularization Models in High-Resolution Feature Space, J. Shi et al., TIP 2018. [PDF]

• [TLcR-RL] Context-Patch based Face Hallucination via Thresholding Locality-Constrained Representation and Reproducing Learning, J. Jiang et al., TCYB 2018. [PDF][Web]

• Face Hallucination via Coarse-to-Fine Recursive Kernel Regression Structure, J. Shi et al. TMM 2019.

• Robust Face Image Super-Resolution via Joint Learning of Subdivided Contextual Model, L. Chen et al. TIP 2019. [PDF]

• SSR2: Sparse signal recovery for single-image super-resolution on faces with extreme low resolutions, R. Abiantun et al. PR 2019. [PDF]

• Robust face hallucination via locality-constrained multiscale coding, L. Liu et al., INS 2020.

• Face hallucination via multiple feature learning with hierarchical structure, L. Liu et al., INS 2020.

• Hallucinating Color Face Image by Learning Graph Representation in Quaternion Space, L. Liu et al., TCYB 2021.

Classical Global Face Methods

• [EigTran] Hallucinating face by eigentransformation, X. Wang et al., TSMC-C 2005 [Web]

• Super-resolution of face images using kernel PCA-based prior, A. Chakrabarti et al., TMM 2007. [PDF]

• A Bayesian Approach to Alignment-Based Image Hallucination, C. Liu et al., ECCV 2012.[PDF]

• A convex approach for image hallucination, P. Innerhofer et al., AAPRW 2013.[Code]

• Structured face hallucination, Y. Yang et al., CVPR 2013.[Web]

• Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior, A. Abbasi et al., [PDF]

Classical Two-Step Methods

• A two-step approach to hallucinating faces: global parametric model and local nonparametric model, C. Liu et al., CVPR 2001.[Web]

• Hallucinating faces: LPH super-resolution and neighbor reconstruction for residue compensation, Y. Zhuang et al., PR 2007.[PDF]

• [CCA] Super-resolution of human face image using canonical correlation analysis, H. Huang et al., PR 2010.[PDF]

Joint Tasks

Joint Face Completion and Super-resolution

• Hallucinating very low-resolution and obscured face images, L. Yang et al., ArXiv 2018.

• FCSR-GAN: End-to-end Learning for Joint Face Completion and Super-resolution, J. Cai et al., FG 2019.

• FCSR-GAN: Joint Face Completion and Super-Resolution via Multi-Task Learning, J. Cai et al., TBIOM 2020.

• [MFG-GAN] Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning, Z. Liu et al., ArXiv 2020.

• [Pro-UIGAN] Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails, Y. Zhang et al., ArXiv 2021.

• [JDSR-GAN] JDSR-GAN: Constructing A Joint and Collaborative Learning Network for Masked Face Super-Resolution, G. Gao et al., ArXiv 2021. [Pdf]

Joint Face Deblurring and Super-resolution

• Learning to Super-Resolve Blurry Face and Text Images, X. Yu et al., ICCV 2017.

• Joint face hallucination and deblurring via structure generation and detail enhancement, Y. Song et al., IJCV 2019.

• [DGFAN] Deblurring And Super-Resolution Using Deep Gated Fusion Attention Networks For Face Images, C. H. Yang et al., ICASSP 2020.

• Super-resolving blurry face images with identity preservation, Y. Xu et al., PRL 2021.

Joint Face Alignment and Super-resolution

• [TDAE] Hallucinating very low-resolution unaligned and noisy face images, X. Yu et al., CVPR 2017. [Web]

• [TDN] Hallucinating very low-resolution unaligned and noisy face images by transformative discriminative autoencoders, X. Yu et al., AAAI 2017.[Web]

• [MTDN] Hallucinating Unaligned Face Images by Multiscale Transformative Discriminative Networks, X. Yu et al., IJCV 2021.

Joint Illumination Compensation and Super-resolution

• [SeLENet] SeLENet: A Semi-Supervised Low Light Face Enhancement Method for Mobile Face Unlock, H. A. Le et al., ICB 2019.

• Learning To See Faces In The Dark，X. Ding et al., ICME 2020.

• [CPGAN] Copy and paste GAN: Face hallucination from shaded thumbnails, Y. Zhang et al., CVPR 2020.

• Recursive Copy and Paste GAN: Face Hallucination from Shaded Thumbnails, Y. Zhang et al., TPAMI 2021.

• Network Architecture Search for Face Enhancement, R. Yasarla et al., ArXiv 2021.

• Deep Illumination-Enhanced Face Super-Resolution Network for Low-Light Images, K. Guo et al., ACM Trans. Multimedia Comput. Commun. Appl. 2022. [Web]

Joint Face Fronlization and Super-resolution

• Can We See More? Joint Frontalization and Hallucination of Unaligned Tiny Faces, X. Yu et al. TPAMI 2019.

• Face Hallucination With Finishing Touches, Y. Zhang et al., TIP 2021.

• Joint Face Image Restoration and Frontalization for Recognition, X. Tu et al., TCSVT 2021.

Related Applications

Face Video Super-resolution

• Face video super-resolution with identity guided generative adversarial networks, D. Li et al., CCCV 2017.

• Super-resolution of Very Low-Resolution Faces from Videos, E. Ataer-Cansizoglu et al., BMVC 2018.

• Video Face Super-Resolution with Motion-Adaptive Feedback Cell, J. Xin et al., AAAI 2020.

• Self-Enhanced Convolutional Network for Facial Video Hallucination, C. Fang et al., TIP 2020.

• VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots, Y. GAN et al., ArXiv 2021.

• [MDVDNet] Multi-modality Deep Restoration of Extremely Compressed Face Videos, X. Zhang et al., ArXiv 2021.

Old Photo Restoration

• [BOPBL] Bringing Old Photos Back to Life, Z. Wan et al., CVPR 2020.

Audio-guided FSR

• Learning to Have an Ear for Face Super-Resolution, G. Meishvili et al., CVPR 2020.

3D FSR

• Super-resolution of 3D face, G. Fan et al., ECCV 2006.

• 3D face hallucination from a single depth frame, L. Shu et al., 3DV 2014.

• Robust 3D patch-based face hallucination, C. Qu et al., WACV 2017.

• 3D Face Point Cloud Super-Resolution Network, J. Li et al., IJCB 2021.

Hyperspectral FSR

• [SSANet]From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution, J. Jiang et al., CVPR Workshops 2022. [PDF]

Cross-Domain Face Miniatures

• [DAR-FSR]Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar, P, Li et al., ICCV 2021. [PDF]

Image Quality Measurement

• RMSE, PSNR, SSIM, LPIPS, NIQE, FID

• Face recognition rate

• Mean Opinion Score (MOS)

Databases

Classical databases

• FERET

• CMU-PIE

• CAS-PEAL-R1

• FEI

• CMU+MIT*

• WHU-SCF* The last two databases are collected by myself.

Largescale databases

• EDFace-Celeb-1M

• PFHQ

• CelebA

• Helen

• Menpo

• Widerface

• AFLW

• VGGFace2

• CASIA-WebFace

• WebFace260M is a new million-scale face benchmark, which is constructed for the research community towards closing the data gap behind the industry.

• VFHQ, A High-Quality Dataset and Benchmark for Video Face Super Resolution

• EDFACE-Celeb is a new million-scale dataset, which consists of two sub-datasets (i.e., EDFACE-Celeb-1M and EDFACE-Celeb-150K) for face super-resolution and blind face restoration

Since 2022/5/7