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检索条件"主题词=convolutional sparse coding"
168 条 记 录,以下是121-130 订阅
排序:
First- and Second-Order Methods for Online convolutional Dictionary Learning
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SIAM JOURNAL ON IMAGING SCIENCES 2018年 第2期11卷 1589-1628页
作者: Liu, Jialin Garcia-Cardona, Cristina Wohlbereg, Brendt Yin, Wotao UCLA Dept Math Los Angeles CA 90095 USA Los Alamos Natl Lab CCS Div Los Alamos NM 87545 USA Los Alamos Natl Lab Theoret Div Los Alamos NM 87545 USA
convolutional sparse representations are a form of sparse representation with a structured, translation-invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring ... 详细信息
来源: 评论
An improved method for single image super-resolution based on deep learning
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SIGNAL IMAGE AND VIDEO PROCESSING 2019年 第3期13卷 557-565页
作者: Xie, Chao Liu, Ying Zeng, Weili Lu, Xiaobo Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Jiangsu Peoples R China Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China
This paper strives for presenting an improved method for single image super-resolution based on deep learning, and therefore, a well-designed network structure is proposed by simultaneously considering the merits of c... 详细信息
来源: 评论
Learning a Non-Locally Regularized convolutional sparse Representation for Joint Chromatic and Polarimetric Demosaicking
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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 5029-5044页
作者: Luo, Yidong Zhang, Junchao Shao, Jianbo Tian, Jiandong Ma, Jiayi Cent South Univ Sch Automat Hunan Prov Key Lab Opt Elect Intelligent Measureme Changsha 410083 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China
Division of focal plane color polarization camera becomes the mainstream in polarimetric imaging for it directly captures color polarization mosaic image by one snapshot, so image demosaicking is an essential task. Cu... 详细信息
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convolutional Dictionary Learning: Acceleration and Convergence
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IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年 第4期27卷 1697-1712页
作者: Chun, Il Yong Fessler, Jeffrey A. Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48019 USA
convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on t... 详细信息
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convolutional Dictionary Learning With Grid Refinement
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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 2558-2573页
作者: Song, Andrew H. Flores, Francisco J. Ba, Demba MIT Elect Engn & Comp Sci 77 Massachusetts Ave Cambridge MA 02139 USA Massachusetts Gen Hosp MGH Boston MA 02114 USA Harvard Med Sch Boston MA 02114 USA Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA
Given a continuous-domain signal that can be modeled as the superposition of localized events from multiple sources, the goal of convolutional Dictionary Learning (CDL) is to identify the location of the events-by Con... 详细信息
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sparse Overcomplete Denoising: Aggregation Versus Global Optimization
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IEEE SIGNAL PROCESSING LETTERS 2017年 第10期24卷 1468-1472页
作者: Carrera, Diego Boracchi, Giacomo Foi, Alessandro Wohlberg, Brendt Politecn Milan I-20133 Milan Italy Tampere Univ Technol Tampere 33720 Finland Los Alamos Natl Lab Los Alamos NM 87545 USA
Denoising is often addressed via sparse coding with respect to an overcomplete dictionary. There are two main approaches when the dictionary is composed of translates of an orthonormal basis. The first, traditionally ... 详细信息
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Edge-assisted Object Segmentation Using Multimodal Feature Aggregation and Learning
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ACM TRANSACTIONS ON SENSOR NETWORKS 2024年 第1期20卷 1-22页
作者: Li, Jianbo Yuan, Genji Yang, Heng Qingdao Univ 308 Ningxia Rd Qingdao Peoples R China Tsinghua Univ 30 Shuangqing Rd Beijing Peoples R China
Object segmentation aims to perfectly identify objects embedded in the surrounding environment and has a wide range of applications. Most previous methods of object segmentation only use RGB images and ignore geometri... 详细信息
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Learning Multiscale convolutional Dictionaries for Image Reconstruction
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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2022年 8卷 425-437页
作者: Liu, Tianlin Chaman, Anadi Belius, David Dokmanic, Ivan Univ Basel Dept Math & Comp Sci CH-4001 Basel Switzerland Univ Illinois Dept Elect Comp Engn Urbana IL 61801 USA
convolutional neural networks (CNNs) have been tremendously successful in solving imaging inverse problems. To understand their success, an effective strategy is to construct simpler and mathematically more tractable ... 详细信息
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Single Image Deraining With Continuous Rain Density Estimation
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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 443-456页
作者: Yu, Lei Wang, Bishan He, Jingwei Xia, Gui-Song Yang, Wen Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China
Single image deraining (SIDR) often suffers from over/under deraining due to the nonuniformity of rain densities and the variety of raindrop scales. In this paper, we propose a continuous density-guided network (CODE-... 详细信息
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Factorized Tensor Dictionary Learning for Visual Tensor Data Completion
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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 1225-1238页
作者: Xu, Ruotao Xu, Yong Quan, Yuhui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Commun & Comp Network Lab Guangdong Guangzhou 510006 Peoples R China Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China
This paper aims at developing a dictionary-learning-based method for completing the visual tensor data with missing elements. Traditional dictionary learning approaches suffer from very high computational costs when p... 详细信息
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