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检索条件"主题词=Convolutional sparse coding"
169 条 记 录,以下是161-170 订阅
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A two-stage convolutional sparse prior model for image restoration
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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2017年 第Oct.期48卷 268-280页
作者: Xiong, Jiaojiao Liu, Qiegen Wang, Yuhao Xu, Xiaoling Nanchang Univ Dept Elect Informat Engn Nanchang Jiangxi Peoples R China
Image restoration (IR) from noisy, blurred or/and incomplete observed measurement is one of the important tasks in image processing community. Image prior is of utmost importance for recovering a high quality image. I... 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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convolutional sparse Representations with Gradient Penalties
Convolutional Sparse Representations with Gradient Penalties
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IEEE International Conference on Acoustics, Speech and Signal Processing
作者: Brendt Wohlberg Theoretical Division Los Alamos National Laboratory Los Alamos NM 87545 USA
While convolutional sparse representations enjoy a number of useful properties, they have received limited attention for image reconstruction problems. The present paper compares the performance of block-based and con... 详细信息
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SYNTHESIS-ANALYSIS DEconvolutional NETWORK FOR COMPRESSED SENSING  24
SYNTHESIS-ANALYSIS DECONVOLUTIONAL NETWORK FOR COMPRESSED SE...
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24th IEEE International Conference on Image Processing (ICIP)
作者: Liu, Qiegen Leung, Henry Nanchang Univ Sch Elect Informat Engn Nanchang 330031 Jiangxi Peoples R China Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada
Synthesis learning and analysis learning, with sparse coding (SC) and Markov random fields (MRFs) as two representative types of models, are two complementary tools to describe the image manifolds. SC has strengths in... 详细信息
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LEVERAGING 2D AND 3D CUES FOR FINE-GRAINED OBJECT CLASSIFICATION  23
LEVERAGING 2D AND 3D CUES FOR FINE-GRAINED OBJECT CLASSIFICA...
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23rd IEEE International Conference on Image Processing (ICIP)
作者: Wang, Xiaolong Li, Robert (Bo) Currey, Jon Samsung Res Amer Mountain View CA 94043 USA
Objects in fine-grained categories always share a high degree of shape similarity, making both "localizing discriminative parts" and "learning appearance descriptors" extremely difficult. We propos... 详细信息
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convolutional sparse REPRESENTATIONS AS AN IMAGE MODEL FOR IMPULSE NOISE RESTORATION  12
CONVOLUTIONAL SPARSE REPRESENTATIONS AS AN IMAGE MODEL FOR I...
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12th IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)
作者: Wohlberg, Brendt Los Alamos Natl Lab Div Theoret Los Alamos NM 87545 USA
Standard sparse representations, applied independently to a set of overlapping image blocks, are a very effective approach to a wide variety of image reconstruction problems. convolutional sparse representations, whic... 详细信息
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BOUNDARY HANDLING FOR convolutional sparse REPRESENTATIONS  23
BOUNDARY HANDLING FOR CONVOLUTIONAL SPARSE REPRESENTATIONS
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23rd IEEE International Conference on Image Processing (ICIP)
作者: Wohlberg, Brendt Los Alamos Natl Lab Div Theoret Los Alamos NM 87545 USA
convolutional sparse representations differ from the standard form in representing the signal to be decomposed as the sum of a set of convolutions with dictionary filters instead of a linear combination of dictionary ... 详细信息
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LEVERAGING 2D AND 3D CUES FOR FINE-GRAINED OBJECT CLASSIFICATION
LEVERAGING 2D AND 3D CUES FOR FINE-GRAINED OBJECT CLASSIFICA...
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IEEE International Conference on Image Processing
作者: Xiaolong Wang Robert (Bo) Li Jon Currey Samsung Research America
Objects in fine-grained categories always share a high degree of shape similarity, making both "localizing discriminative parts" and "learning appearance descriptors" extremely difficult. We propos... 详细信息
来源: 评论
Learning Separable Filters
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2015年 第1期37卷 94-106页
作者: Sironi, Amos Tekin, Bugra Rigamonti, Roberto Lepetit, Vincent Fua, Pascal Ecole Polytech Fed Lausanne IC Fac Comp Vis Lab CH-1015 Lausanne Switzerland Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria
Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usual... 详细信息
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