Blind Face Restoration via Deep Multi-scale Component Dictionaries
Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算学部）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Peng Cheng Lab, Shenzhen（鹏城实验室）
Recent reference-based face restoration methods have received considerable attention due to their great capability in recovering high-frequency details on real low-quality images. However, most of these methods require a high-quality reference image of the same identity, making them only applicable in limited scenes. To address this issue, this paper suggests a deep face dictionary network (termed as DFDNet) to guide the restoration process of degraded observations. To begin with, we use K-means to generate deep dictionaries for perceptually significant face components (\ie, left/right eyes, nose and mouth) from high-quality images. Next, with the degraded input, we match and select the most similar component features from their corresponding dictionaries and transfer the high-quality details to the input via the proposed dictionary feature transfer (DFT) block. In particular, component AdaIN is leveraged to eliminate the style diversity between the input and dictionary features (\eg, illumination), and a confidence score is proposed to adaptively fuse the dictionary feature to the input. Finally, multi-scale dictionaries are adopted in a progressive manner to enable the coarse-to-fine restoration. Experiments show that our proposed method can achieve plausible performance in both quantitative and qualitative evaluation, and more importantly, can generate realistic and promising results on real degraded images without requiring an identity-belonging reference. The source code and models are available at \url{https://github.com/csxmli2016/DFDNet}.
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
Towards Real-World Blind Face Restoration with Generative Diffusion Prior
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；College of Engineering and Computer Science, Australian National University（澳大利亚国立大学工程与计算机科学学院）
Survey on Deep Face Restoration: From Non-blind to Blind and Beyond
Beijing University of Posts and Telecommunications（北京邮电大学）；ETH Zürich（苏黎世联邦理工学院）；Shanghai University（上海大学）；Nanyang Technological University（南洋理工大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；National Tsing Hua University（国立清华大学）
Enhancing Blind Face Restoration through Online Reinforcement Learning
Institute of Information Science, Beijing Jiaotong University（信息科学学院，北京交通大学）；Visual Intelligence +X International Cooperation Joint Laboratory of MOE（教育部视觉智能+X国际合作联合实验室）；Kuaishou Technology（快手科技）
BIR-Adapter: A parameter-efficient diffusion adapter for blind image restoration
Chair of Media Technology, Munich Institute of Robotics and Machine Intelligence（媒体技术教授会，慕尼黑机器人与机器智能研究所）；School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）
Analysis and Benchmarking of Extending Blind Face Image Restoration to Videos
The University of Hong Kong（香港大学）；SenseTime Research（商汤科技研究院）；ARC Lab, Tencent PCG（腾讯平台与内容事业群ARC实验室）；The Guangdong University of Technology（广东工业大学）；Texas A&M University（德克萨斯农工大学）
Bridging Information Asymmetry: A Hierarchical Framework for Deterministic Blind Face Restoration
Biomedical Engineering Department, College of Future Technology, Peking University（北京大学未来技术学院生物医学工程系）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究所）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）
Discrete Prior-based Temporal-coherent Content Prediction for Blind Face Video Restoration
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Institute of Super Robotics(Huangpu)（超级机器人（黄埔）研究院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
Nankai University（南开大学）；Tencent PCG（腾讯平台与内容事业群）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shanghai AI Laboratory（上海人工智能实验室）；Tencent Online Video（腾讯在线视频）
AuthFace: Towards Authentic Blind Face Restoration with Face-oriented Generative Diffusion Prior
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；vivo Mobile Communication Co., Ltd（vivo移动通信有限公司）；Nanyang Technological University（南洋理工大学）
InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration
Nankai University（南开大学）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Linkoping University（林雪平大学）；SB Intuitions, SoftBank（软银SB Intuitions）；Nankai International Advanced Research Institute (Shenzhen Futian), Nankai University（南开大学深圳福田国际先进研究院）
