Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution
Applied Research Center (ARC), Tencent PCG（腾讯平台与内容事业群应用研究中心）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）
Recent blind super-resolution (SR) methods typically consist of two branches, one for degradation prediction and the other for conditional restoration. However, our experiments show that a one-branch network can achieve comparable performance to the two-branch scheme. Then we wonder: how can one-branch networks automatically learn to distinguish degradations? To find the answer, we propose a new diagnostic tool -- Filter Attribution method based on Integral Gradient (FAIG). Unlike previous integral gradient methods, our FAIG aims at finding the most discriminative filters instead of input pixels/features for degradation removal in blind SR networks. With the discovered filters, we further develop a simple yet effective method to predict the degradation of an input image. Based on FAIG, we show that, in one-branch blind SR networks, 1) we are able to find a very small number of (1%) discriminative filters for each specific degradation; 2) The weights, locations and connections of the discovered filters are all important to determine the specific network function. 3) The task of degradation prediction can be implicitly realized by these discriminative filters without explicit supervised learning. Our findings can not only help us better understand network behaviors inside one-branch blind SR networks, but also provide guidance on designing more efficient architectures and diagnosing networks for blind SR.
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution
Beijing Normal University（北京师范大学）；Xidian University（西安电子科技大学）；Xinjiang Medical University（新疆医科大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds
The HongKong Polytechnic University（香港理工大学）；Shenzhen Institute of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai AI Laboratory（上海人工智能实验室）
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution
School of Control and Computer Engineering, North China Electric Power University（华北电力大学控制与计算机工程学院）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
From General to Specific: Online Updating for Blind Super-Resolution
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
The University of Hong Kong（香港大学）；Xi’an Jiaotong University（西安交通大学）；Baidu Research（百度研究院）；The Hong Kong Polytechnic University（香港理工大学）；Peng Cheng Laboratory（鹏城实验室）
基于模型的单图像盲超分辨率（SISR）研究近年来取得了巨大的成就，但大多没有充分考虑图像退化的影响。首先，他们总是假设图像噪声服从独立同分布（i.i.d.）高斯或拉普拉斯分布，这在很大程度上低估了真实噪声的复杂性。其次，以前常用的内核先验（例如，标准化、稀疏性）不足以有效地保证合理的内核解决方案，从而降低了后续SISR任务的性能。针对上述问题，本文提出了一种基于模型的概率框架下的盲SISR方法，从噪声和模糊核的角度对图像退化进行建模。具体来说，不是传统的i.i.d.噪声假设，基于块的非i.i.d.提出了一种噪声模型来处理复杂的实际噪声，期望增加模型的自由度来表示噪声。对于模糊核，我们新颖地构造了一个简洁而有效的核生成器，并将其作为显式核先验（EKP）插入到所提出的盲SISR方法中。为了解决所提出的模型，一个理论上接地Monte Carlo EM算法是专门设计的。综合实验表明，我们的方法优于当前国家的最先进的合成和真实数据集。源代码可在https://github.com/zsyOAOA/BSRDM上获得。
LightBSR: Towards Lightweight Blind Super-Resolution via Discriminative Implicit Degradation Representation Learning
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，中国科学院自动化研究所）；North China Electric Power University（华北电力大学）；Beijing Jiaotong University（北京交通大学）；YunQue AGI, Hangzhou, China（云阙AGI，杭州，中国）
From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
Harbin Institute of Technology（哈尔滨工业大学）；The Hong Kong Polytechnic University（香港理工大学）；Nanyang Technological University（南洋理工大学）；Alibaba Group（阿里巴巴集团）；Peng Cheng Lab（鹏城实验室）
Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations
VCIP, College of Computer Science, Nankai University, Tianjin, 300350, China（VCIP，计算机科学学院，南开大学，天津，300350，中国）；School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University, Shenzhen 518107, P.R. China（网络安全科学与技术学院，深圳校区，中山大学，深圳 518107，中华人民共和国中国）；College of Artificial Intelligence, Nankai University, Tianjin, 300350, China（人工智能学院，南开大学，天津，300350，中国）；Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, China（集成物流支持科学技术实验室，国防科技大学，长沙 410073，中国）
Learning the Degradation Distribution for Blind Image Super-Resolution
University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；National Laboratory of Pattern Recognition (NLPR)（模式识别国家重点实验室）；Center for Research on Intelligent Perception and Computing (CRIPAC)（智能感知与计算研究中心）
Expanding Synthetic Real-World Degradations for Blind Video Super Resolution
K|Lens GmbH（K|Lens有限公司）；Saarland Informatics Campus（萨尔兰信息学园区）；IIT Jammu（印度理工学院查谟分校）；MPI Informatics（马克斯·普朗克信息学研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Applied Research Center (ARC), Tencent PCG（腾讯PCG应用研究中心）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai AI Laboratory（上海人工智能实验室）
The University of Hong Kong（香港大学）；The University of Sydney（悉尼大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Deep Variational Network Toward Blind Image Restoration
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）；Macau Institute of Systems Engineering, Macau University of Science and Technology（澳门科技大学澳门系统工程研究所）
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）
