We consider the problem of denoising with the help of prior information taken from a database of clean signals or images. Denoising with variational methods is very efficient if a regularizer well adapted to the nature of the data is available. Thanks to the maximum a posteriori Bayesian framework, such regularizer can be systematically linked with the distribution of the data. With deep neural networks (DNN), complex distributions can be recovered from a large training database.To reduce the computational burden of this task, we adapt the compressive learning framework to the learning of regularizers parametrized by DNN. We propose two variants of stochastic gradient descent (SGD) for the recovery of deep regularization parameters from a heavily compressed database. These algorithms outperform the initially proposed method that was limited to low-dimensional signals, each iteration using information from the whole database. They also benefit from classical SGD convergence guarantees. Thanks to these improvements we show that this method can be applied for patch based image denoising.}
Robust and interpretable blind image denoising via bias-free convolutional neural networks
Center for Data Science（数据科学中心）；New York University（纽约大学）；Center for Neural Science（神经科学中心）；Howard Hughes Medical Institute（霍华德·休斯医学研究所）；Courant Inst. of Mathematical Sciences（库朗数学科学研究所）
Monotonically Convergent Regularization by Denoising
Washington University in St. Louis（圣路易斯华盛顿大学）；Department of Electrical and Systems Engineering（电气与系统工程系）；Department of Computer Science and Engineering（计算机科学与工程系）
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Harbin Institute of Technology（哈尔滨工业大学）；The Hong Kong Polytechnic University（香港理工大学）；Graz University of Technology（格拉茨技术大学）；Xi’an Jiaotong University（西安交通大学）
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces
Beijing Institute of Technology（北京理工大学）；CNRS（法国国家科学研究中心）；CentraleSupélec（中央理工-高等电力学院）；Université Paris-Saclay（巴黎-萨克雷大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
该研究提出 SSUMamba，通过六种空间-光谱连续扫描顺序、双向 SSM 与 3D 卷积高效建模高光谱图像的长程及局部依赖，在降低每批内存消耗的同时提升去噪性能。
Deep LoRA-Unfolding Networks for Image Restoration
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（计算机科学与技术学院，哈尔滨工业大学（深圳））；Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（图像通信与网络工程院，上海交通大学）；School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院，南京大学）；School of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen)（电子与信息工程学院，哈尔滨工业大学（深圳））
Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser
Center for Data Science（数据科学中心）；Howard Hughes Medical Institute（霍华德·休斯医学研究所）；New York University（纽约大学）；Center for Neural Science（神经科学中心）；Courant Inst. of Mathematical Sciences（柯朗数学科学研究所）
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration
College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications（南京邮电大学电子与光学工程学院、微电子学院）；College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications（南京邮电大学通信与信息工程学院）
A Plug-and-Play Framework for Volumetric Light-Sheet Image Reconstruction
Department of Mathematics（数学系）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Department of Bioengineering（生物工程系）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation
UC Berkeley（伯克利大学）；University of Tsukuba（筑波大学）；International Christian University（国际基督教大学）；Toyo University（东洋大学）；Tokyo Institute of Technology（东京技术大学）；Tohoku University（东北大学）；MIT（麻省理工学院）
Exploring User-level Gradient Inversion with a Diffusion Prior
Vanderbilt University（范德堡大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Mitsubishi Electric Research Laboratories（三菱电机研发实验室）；Vanderbilt University Medical Center（范德堡大学医学中心）
Graph Information Bottleneck for Subgraph Recognition
NLPR&CRIPAC, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别与智能控制国家重点实验室（NLPR&CRIPAC））；University of Chinese Academy of Sciences（中国科学院大学）；Tencent AI LAB（腾讯人工智能实验室）；Center for Excellence in Brain Science and Intelligence Technology, CAS（中国科学院脑科学与智能技术卓越创新中心）
Unsupervised CP-UNet Framework for Denoising DAS Data with Decay Noise
School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan)（中国地质大学（武汉）机械工程与电子信息学院）；Shenzhen Research Institute of China University of Geosciences（中国地质大学深圳研究院）；Yazheng Technology Group Co., Ltd（雅正科技集团有限公司）；China Electric Power Planning & Engineering Institute（中国电力规划设计总院）；Liuzhou Wuling New Energy Automobile Co., Ltd（柳州五菱新能源汽车有限公司）；Yangtze optical fiber and cable Joint Stock Limited Company（长飞光纤光缆股份有限公司）；School of Economics and Management, China University of Geosciences (Wuhan)（中国地质大学（武汉）经济管理学院）
本文提出无监督 CP-UNet 框架，通过 Context Pyramid Module、CM、LN 与 Huber-loss 抑制 DAS 数据中的随机和不规则噪声，且无需标签，实验显示其降噪性能优于传统方法和现有 UL 框架。
