A list of face face super-resolution/hallucination resources collected by Junjun Jiang. If you find these resources are useful, please cite our following 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, 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.
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Hallucinating face, S. Baker and T. Kanade, FG 2000. [PDF]
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[NE] Super-resolution through neighbor embedding, Chang et al. CVPR 2004. [Web]
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[LSR] Hallucinating face by position-patch, Ma et al., PR 2010. [Web]
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[SR] Position-patch based face hallucination using convex optimization, C. Jung et al., SPL 2010. [Web]
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[LcR] Noise robust face hallucination via locality-constrained representation, J. Jiang et al., TMM 2014.[Web]
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[LINE] Multilayer Locality-Constrained Iterative Neighbor Embedding, J. Jiang et al., TIP 2014. [Web]
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Face Hallucination Using Linear Models of Coupled Sparse Support, R. A. Farrugia et al., TIP 2017.[PDF][Web]
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Hallucinating Face Image by Regularization Models in High-Resolution Feature Space, J. Shi et al., TIP 2018. [PDF]
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[TLcR-RL] Context-Patch based Face Hallucination via Thresholding Locality-Constrained Representation and Reproducing Learning, J. Jiang et al., TCYB 2018. [PDF][Web]
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Face Hallucination via Coarse-to-Fine Recursive Kernel Regression Structure, J. Shi et al. TMM 2019.
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Robust Face Image Super-Resolution via Joint Learning of Subdivided Contextual Model, L. Chen et al. TIP 2019. [PDF]
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SSR2: Sparse signal recovery for single-image super-resolution on faces with extreme low resolutions, R. Abiantun et al. PR 2019. [PDF]
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Robust face hallucination via locality-constrained multiscale coding, L. Liu et al., INS 2020.
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Face hallucination via multiple feature learning with hierarchical structure, L. Liu et al., INS 2020.
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Hallucinating Color Face Image by Learning Graph Representation in Quaternion Space, L. Liu et al., TCYB 2021.
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[EigTran] Hallucinating face by eigentransformation, X. Wang et al., TSMC-C 2005 [Web]
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Super-resolution of face images using kernel PCA-based prior, A. Chakrabarti et al., TMM 2007. [PDF]
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A Bayesian Approach to Alignment-Based Image Hallucination, C. Liu et al., ECCV 2012.[PDF]
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A convex approach for image hallucination, P. Innerhofer et al., AAPRW 2013.[Code]
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Structured face hallucination, Y. Yang et al., CVPR 2013.[Web]
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Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior, A. Abbasi et al., [PDF]
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A two-step approach to hallucinating faces: global parametric model and local nonparametric model, C. Liu et al., CVPR 2001.[Web]
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Hallucinating faces: LPH super-resolution and neighbor reconstruction for residue compensation, Y. Zhuang et al., PR 2007.[PDF]
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[CCA] Super-resolution of human face image using canonical correlation analysis, H. Huang et al., PR 2010.[PDF]
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[BCCNN] Learning Face Hallucination in the Wild, E. Zhou et al., AAAI 2015.
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[URDGN] Ultra-resolving face images by discriminative generative networks, X. Yu et al., CVPR 2016. [Web]
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[SRCNN-IBP] Face Hallucination Using Convolutional Neural Network with Iterative Back Projection, D. Huang et al., CCBR 2016.
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[GLN] Global-Local Face Upsampling Network, O. Tuzel et al., ArXiv 2016.
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[GLFSR] Global-local fusion network for face super-resolution, Tao Lu et al., Neurocomputing 2020.
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Patch-based face hallucination with multitask deep neural network, W. Ko et al., ICME 2016.
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Face hallucination by deep traversal network, Z. Feng et at., ICPR 2016.
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Face hallucination using region-based deep convolutional networks, T. Lu et al., ICIP 2017.
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Face Super-Resolution Through Wasserstein GANs. Z. Chen et al., ArXiv 2017.
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High-Quality Face Image SR Using Conditional Generative Adversarial Networks, B. Huang et al., ArXiv 2017.
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[WaSRNet] Wavelet-SRNet: A Wavelet-Based CNN for Multi-Scale Face Super Resolution, H. Huang et al., ICCV 2017.
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[Attention-FH] Attention-Aware Face Hallucination via Deep Reinforcement Learning, Q. Cao et al., CVPR 2017. [PDF][Web]
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Super-resolution Reconstruction of Face Image Based on Convolution Network, W. Huang et al., AISC 2018.
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A Noise Robust Face Hallucination Framework Via Cascaded Model of Deep Convolutional Networks and Manifold Learning, L. Han et al., ICME 2018
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Face Hallucination via Convolution Neural Network, H. Nie et al., ICTAI 2018.
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Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning, Yukai Shi et al. TPAMI 2019.
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Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement, Yibing Song et al. IJCV 2019. [Web]
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Sequential Gating Ensemble Network for Noise Robust Multiscale Face Restoration, Z. chen et al., TCYB 2019.
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Face Image Super-Resolution Using Inception Residual Network and GAN Framework, S. D. Indradi et al., ICOICT 2019.
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Guided Cyclegan Via Semi-Dual Optimal Transport for Photo-Realistic Face Super-Resolution, W. Zheng et al., ICIP 2019.
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ATMFN: Adaptive-threshold-based Multi-model Fusion Network for Compressed Face Hallucination, K. Jiang et al., TMM 2019.
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[SRDSI] Face hallucination from low quality images using definition-scalable inference, X. Hu et al. PR 2019.
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RBPNET: An asymptotic Residual Back-Projection Network for super-resolution of very low-resolution face image, X. Wang et al., Neurocomputing 2020.
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Efficient Face Super-Resolution Based on Separable Convolution Projection Networks, X. Chen et al., CRC 2020.
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A Densely Connected Face Super-Resolution Network Based on Attention Mechanism, Y. Liu et al., ICIEA 2020.
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[HiFaceGAN] Implicit Subspace Prior Learning for Dual-Blind Face Restoration, L. Yang et al., ArXiv 2020.
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Super-resolving Tiny Faces with Face Feature Vectors, Y. Lu et al., ICIST 2020.
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[SPARNet]Learning Spatial Attention for Face Super-Resolution, C. Chen et al., TIP 2020. [Web]
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PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution, H. Du et al., ACM MM 2020.
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[SPGAN] Supervised Pixel-Wise GAN for Face Super-Resolution, M. Zhang et al., TMM 2020.
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Robust Super-Resolution of Real Faces using Smooth Features, S. Goswami et al., ECCVW 2020.
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Learning wavelet coefficients for face super-resolution, Y. Liu et al., VC 2020.
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PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models, S. Memon et al,. CVPR 2020.
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Characteristic Regularisation for Super-Resolving Face Images, Z. Cheng et al., WACV 2020.
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[DPDFN] Dual-path deep fusion network for face image hallucination, K. Jiang, TMM 2020.
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GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution, K. C. K. Chan et al., CVPR 2021.
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[GFP-GAN] Towards Real-World Blind Face Restoration with Generative Facial Prior, X. Wang et al., CVPR 2021.
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[GPEN] GAN Prior Embedded Network for Blind Face Restoration in the Wild, T. Yang et al., CVPR 2021.
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Generative Facial Prior for Large-Factor Blind Face Super-Resolution, X Gua et al., ICAITA 2021. [Pdf]
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E-ComSupResNet: Enhanced Face Super-Resolution Through Compact Network, E. Chudasama et al., TBIOM 2021.
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[MLGE] Multi-Laplacian GAN with Edge Enhancement for Face Super Resolution, S. Ko et al., ICPR 2021.
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[TANet] TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network, Z. Wang et al., ArXiv 2021.[PDF]
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Face Hallucination via Split-Attention in Split-Attention Network, T. Lu et al., ACMMM 2021. [Web]
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[SelFSR] SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network, X. Zeng et al., ArXiv 2021. [PDF]
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[FRGAN] FRGAN: A Blind Face Restoration with Generative Adversarial Networks, T. Wei et al., MPE 2021 [PDF]
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[Panini-Net] Panini-Net: GAN Prior based Degradation-Aware Feature Interpolation for Face Restoration, Y. Wang et al., AAAI 2022.
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[GCFSR] GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors, J. He et al., CVPR2022 [PDF]
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[CBN] Deep cascaded bi-network for face hallucination, S. Zhu et al., ECCV 2016. [PDF][Web]
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[KPEFH] Face Hallucination Based on Key Parts Enhancement, K. Li et al., ICASSP 2018.
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[LCGE] Learning to hallucinate face images via component generation and enhancement, Y. Song et al., IJCAI 2017 [PDF][Web]
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[MNCEFH] Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination, J. Jiang et al., IJCAI 2018. [PDF][Web]
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[FSRNet] FSRNet: End-to-End learning face super-resolution with facial priors, Y. Chen et al., CVPR 2018. [PDF][Web]
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[FSRGFCH] Face super-resolution guided by facial component heatmaps, ECCV 2018, X. Yu et al. [PDF] [Web]
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A coarse-to-fine face hallucination method by exploiting facial prior knowledge, ICIP 2018, Mengyan Li et al. [PDF][Web]
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[PFSRNet] Progressive Face Super-Resolution via Attention to Facial Landmark, D. Kim et al., BMVC 2019. [PDF][Code]
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[JASRNet] Joint Super-Resolution and Alignment of Tiny Faces, Y. Yin et al. AAAI 2019.
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Component Attention Guided Face Super-Resolution Network: CAGFace, R. Kalarot et al., WACV 2020.
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SAAN: Semantic Attention Adaptation Network for Face Super-Resolution, T. Zhao et al., ICME 2020.
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[PMGMSAN] Parsing Map Guided Multi-Scale Attention Network For Face Hallucination, C. Wang et al., ICASSP 2020.
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[ATSENet] Learning Face Image Super-Resolution through Facial Semantic Attribute Transformation and Self-Attentive Structure Enhancement, M. Li et al., TMM 2020.
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[DIC] Deep Face Super-Resolution With Iterative Collaboration Between Attentive Recovery and Landmark Estimation, Cheng Ma et al., CVPR 2020.
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MSFSR: A Multi-Stage Face Super-Resolution with Accurate Facial Representation via Enhanced Facial Boundaries, Y. Zhang et al., CVPRW 2020.
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Semantic-driven Face Hallucination Based on Residual Network, X. Yu et al., TBIOM 2021
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Progressive Semantic-Aware Style Transformation for Blind Face Restoration, C. Chen et al., CVPR 2021
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[HapFSR] Heatmap-Aware Pyramid Face Hallucination, C. Wang et al. ICME 2021.
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[OBC-FSR] Organ-Branched CNN for Robust Face Super-Resolution, J. Li et al., ICME 2021.
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[HCRF] Features Guided Face Super-Resolution via Hybrid Model of Deep Learning and Random Forests, Z. S. Liu et al., TIP 2021.
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DCLNet: Dual Closed-loop Networks for face super-resolution, H. Wang et al., KBS 2021.
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Progressive face super-resolution with cascaded recurrent convolutional network, S. Liu et al., Neurocomputing 2021.
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Face Super-Resolution Network with Incremental Enhancement of Facial Parsing Information, S. Liu et al., ICPR 2021.
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Unsupervised face super-resolution via gradient enhancement and semantic guidance, L. Li et al., VC 2021.
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SemFSR: An Unsupervised Face SR with Semantic Features for Multiple Degradations, H. Qi et al., ICTAI 2021.
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Face Restoration via Plug-and-Play 3D Facial Priors, X. Hu et al., TPAMI 2021, [PDF]
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Blind Face Restoration via Multi-Prior Collaboration and Adaptive Feature Fusion, Z. Teng et al., FNBOT 2022 [Web]
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Blind Face Restoration via Integrating Face Shape and Generative Priors, F. Zhu et al., CVPR 2022.
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Propagating Facial Prior Knowledge for Multi-Task Learning in Face Super-Resolution, C. Wang et al., TCSVT 2022. [Code]
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[FaceAttr] Super-resolving very low-resolution face images with supplementary attributes, CVPR2018, Xin Yu et al. [PDF][Web]
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Attribute-Guided Face Generation Using Conditional CycleGAN, ECCV2018, Yongyi Lu et al. [PDF][Web]
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Attribute Augmented Convolutional Neural Network for Face Hallucination, CVPRW2018, Cheng-Han Lee et al. [PDF][Web]
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Residual Attribute Attention Network for Face Image Super-Resolution, Jingwei Xin et al. AAAI2019. [PDF]
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[ATNet] Deep Learning Face Hallucination via Attributes Transfer and Enhancement, M. Li et al., ICME 2019.
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[FACN] Facial Attribute Capsules for Noise Face Super Resolution, J. Xin et al., AAAI 2020.
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[ATSENet] Learning Face Image Super-Resolution through Facial Semantic Attribute Transformation and Self-Attentive Structure Enhancement, M. Li et al., TMM 2020.
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[AGA-GAN] AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination, A. Srivastava et al. ArXiv 2021. [PDF]
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[SICNN] Super-Identity Convolutional Neural Network for Face Hallucination, K. Zhang et al., ECCV 2018. [PDF][Web]
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[FH-GAN] FH-GAN: Face Hallucination and Recognition Using Generative Adversarial Network, B. Bayramli et al., NIP 2019.
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[WaSRGAN] Wavelet domain generative adversarial network for multi-scale face hallucination, H. Huang et al., IJCV 2019. [Code]
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Low-Resolution Face Recognition Based on Identity-Preserved Face Hallucination, S. Lai et al., ICIP 2019.
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[IPFH] Identity-Preserving Face Hallucination via Deep Reinforcement Learning, X. Cheng et al., TCSVT 2019.
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Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-resolution Network, E. Ataer-Cansizoglu et al., ArXiv 2019.
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Optimizing Super Resolution for Face Recognition, A. A. Abello et al., SIBGRAPI 2019.
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SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination, C.Hsu et al., TIP 2019. [Code]
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[IADFH] Identity-Aware Deep Face Hallucination via Adversarial Face Verification, H. Kazemi et al., BTAS 2019.
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[C-SRIP] Face Hallucination Using Cascaded Super-Resolution and Identity Priors, K. Grm et al., TIP 2020.
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[SPGAN] Supervised Pixel-Wise GAN for Face Super-Resolution, M. Zhang et al., TMM 2020.
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Identity-Aware Face Super-Resolution for Low-Resolution Face Recognition, J. Chen et al., SPL 2020.
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Face Super-Resolution Through Dual-Identity Constraint, F. Cheng et al., ICME 2021.
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Edge and identity preserving network for face super-resolution, J. Kim et al., Neurocomputing 2021.
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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]
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[GFRNet] Learning Warped Guidance for Blind Face Restoration, X. Li et al., ECCV 2019.
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[GWAInet] Exemplar Guided Face Image Super-Resolution without Facial Landmarks, CVPRW 2019.
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[JSRFC] Recovering Extremely Degraded Faces by Joint Super-Resolution and Facial Composite, X. Li et al., ICTAI 2019.
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[ASFFNet] Enhanced Blind Face Restoration With Multi-Exemplar Images and Adaptive Spatial Feature Fusion, X. Li et al., CVPR 2020.[Web]
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[MEFSR] Multiple Exemplars-based Hallucination for Face Super-resolution and Editing, K. Wang et al., ACCV 2020.
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[DFDNet] Blind Face Restoration via Deep Multi-scale Component Dictionaries, X. Li et al. ECCV 2020. [Web]
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Gluing Reference Patches Together for Face Super-Resolution, J. Kim et al. IEEE Access 2021. [pdf]
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Semantic-Aware Latent Space Exploration for Face Image Restoration, Y. Guo, et al., ICME 2022. [PDF]
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[RestoreFormer] RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs, Z. Wang et al., CVPR 2022 [PDF]
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Rethinking Deep Face Restoration, Y. Zhao et al., CVPR 2022 [PDF]
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[VQFR] VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder, Y. Gu et al., ARXIV 2022. [PDF]
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[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]
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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]
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Real-World Super-Resolution of Face-Images from Surveillance Cameras, A. Aakerberg et al., ArXiv 2021.
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[SCGAN] Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution, Hao Hou et al., arXiv 2022. [PDF][Code]
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Hallucinating very low-resolution and obscured face images, L. Yang et al., ArXiv 2018.
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FCSR-GAN: End-to-end Learning for Joint Face Completion and Super-resolution, J. Cai et al., FG 2019.
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FCSR-GAN: Joint Face Completion and Super-Resolution via Multi-Task Learning, J. Cai et al., TBIOM 2020.
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[MFG-GAN] Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning, Z. Liu et al., ArXiv 2020.
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[Pro-UIGAN] Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails, Y. Zhang et al., ArXiv 2021.
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[JDSR-GAN] JDSR-GAN: Constructing A Joint and Collaborative Learning Network for Masked Face Super-Resolution, G. Gao et al., ArXiv 2021. [Pdf]
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Learning to Super-Resolve Blurry Face and Text Images, X. Yu et al., ICCV 2017.
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Joint face hallucination and deblurring via structure generation and detail enhancement, Y. Song et al., IJCV 2019.
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[DGFAN] Deblurring And Super-Resolution Using Deep Gated Fusion Attention Networks For Face Images, C. H. Yang et al., ICASSP 2020.
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Super-resolving blurry face images with identity preservation, Y. Xu et al., PRL 2021.
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[TDAE] Hallucinating very low-resolution unaligned and noisy face images, X. Yu et al., CVPR 2017. [Web]
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[TDN] Hallucinating very low-resolution unaligned and noisy face images by transformative discriminative autoencoders, X. Yu et al., AAAI 2017.[Web]
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[MTDN] Hallucinating Unaligned Face Images by Multiscale Transformative Discriminative Networks, X. Yu et al., IJCV 2021.
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[SeLENet] SeLENet: A Semi-Supervised Low Light Face Enhancement Method for Mobile Face Unlock, H. A. Le et al., ICB 2019.
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Learning To See Faces In The Dark,X. Ding et al., ICME 2020.
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[CPGAN] Copy and paste GAN: Face hallucination from shaded thumbnails, Y. Zhang et al., CVPR 2020.
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Recursive Copy and Paste GAN: Face Hallucination from Shaded Thumbnails, Y. Zhang et al., TPAMI 2021.
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Network Architecture Search for Face Enhancement, R. Yasarla et al., ArXiv 2021.
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Deep Illumination-Enhanced Face Super-Resolution Network for Low-Light Images, K. Guo et al., ACM Trans. Multimedia Comput. Commun. Appl. 2022. [Web]
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Can We See More? Joint Frontalization and Hallucination of Unaligned Tiny Faces, X. Yu et al. TPAMI 2019.
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Face Hallucination With Finishing Touches, Y. Zhang et al., TIP 2021.
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Joint Face Image Restoration and Frontalization for Recognition, X. Tu et al., TCSVT 2021.
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Face video super-resolution with identity guided generative adversarial networks, D. Li et al., CCCV 2017.
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Super-resolution of Very Low-Resolution Faces from Videos, E. Ataer-Cansizoglu et al., BMVC 2018.
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Video Face Super-Resolution with Motion-Adaptive Feedback Cell, J. Xin et al., AAAI 2020.
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Self-Enhanced Convolutional Network for Facial Video Hallucination, C. Fang et al., TIP 2020.
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VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots, Y. GAN et al., ArXiv 2021.
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[MDVDNet] Multi-modality Deep Restoration of Extremely Compressed Face Videos, X. Zhang et al., ArXiv 2021.
- [BOPBL] Bringing Old Photos Back to Life, Z. Wan et al., CVPR 2020.
- Learning to Have an Ear for Face Super-Resolution, G. Meishvili et al., CVPR 2020.
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Super-resolution of 3D face, G. Fan et al., ECCV 2006.
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3D face hallucination from a single depth frame, L. Shu et al., 3DV 2014.
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Robust 3D patch-based face hallucination, C. Qu et al., WACV 2017.
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3D Face Point Cloud Super-Resolution Network, J. Li et al., IJCB 2021.
- [SSANet]From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution, J. Jiang et al., CVPR Workshops 2022. [PDF]
- [DAR-FSR]Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar, P, Li et al., ICCV 2021. [PDF]
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RMSE, PSNR, SSIM, LPIPS, NIQE, FID
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Face recognition rate
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Mean Opinion Score (MOS)
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WebFace260M is a new million-scale face benchmark, which is constructed for the research community towards closing the data gap behind the industry.
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VFHQ, A High-Quality Dataset and Benchmark for Video Face Super Resolution