Sparse-View CT Reconstruction via Convolutional Sparse Coding Article Swipe

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Peng Bao , Wenjun Xia , Kang Yang , Jiliu Zhou , Yi Zhang ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1810.06228

Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features. To deal with these problems, the convolutional sparse coding (CSC) has been proposed and introduced into various applications. In this paper, inspired by the successful applications of CSC in the field of signal processing, we propose a novel sparse-view CT reconstruction method based on CSC with gradient regularization on feature maps. By directly working on whole image, which need not to divide the image into overlapped patches like dictionary learning based methods, the proposed method can maintain more details and avoid the artifacts caused by patch aggregation. Experimental results demonstrate that the proposed method has better performance than several existing algorithms in both qualitative and quantitative aspects.

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Computer Science

Artificial Intelligence

Convolutional Neural Network

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Computer science Neural coding Artificial intelligence Regularization (linguistics) Dictionary learning Pattern recognition (psychology) Coding (social sciences) Feature (linguistics) Iterative reconstruction Convolutional neural network Sparse matrix Field (mathematics) Image (mathematics) Computer vision Mathematics Gaussian Quantum mechanics Statistics Physics Pure mathematics Philosophy Linguistics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1810.06228
PDF: https://arxiv.org/pdf/1810.06228
OA Status: green
Cited By: 1
References: 10
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.1810.06228

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Title: Sparse-View CT Reconstruction via Convolutional Sparse Coding

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Language: en

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Publication year: 2018

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Publication date: 2018-10-15

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Authors: Peng Bao, Wenjun Xia, Kang Yang, Jiliu Zhou, Yi Zhang

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Landing page: https://arxiv.org/abs/1810.06228

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Concepts: Computer science, Neural coding, Artificial intelligence, Regularization (linguistics), Dictionary learning, Pattern recognition (psychology), Coding (social sciences), Feature (linguistics), Iterative reconstruction, Convolutional neural network, Sparse matrix, Field (mathematics), Image (mathematics), Computer vision, Mathematics, Gaussian, Quantum mechanics, Statistics, Physics, Pure mathematics, Philosophy, Linguistics

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Cited by: 1

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abstract_inverted_index.in	52, 125
abstract_inverted_index.of	20, 50, 55
abstract_inverted_index.on	67, 72, 78
abstract_inverted_index.to	84
abstract_inverted_index.we	58
abstract_inverted_index.CSC	51, 68
abstract_inverted_index.and	9, 37, 103, 128
abstract_inverted_index.are	7
abstract_inverted_index.can	99
abstract_inverted_index.has	34, 118
abstract_inverted_index.not	83
abstract_inverted_index.the	10, 21, 29, 47, 53, 86, 96, 105, 115
abstract_inverted_index.been	35
abstract_inverted_index.both	126
abstract_inverted_index.deal	25
abstract_inverted_index.into	39, 88
abstract_inverted_index.like	91
abstract_inverted_index.more	101
abstract_inverted_index.need	82
abstract_inverted_index.same	22
abstract_inverted_index.than	121
abstract_inverted_index.that	114
abstract_inverted_index.this	43
abstract_inverted_index.with	13, 26, 69
abstract_inverted_index.(CSC)	33
abstract_inverted_index.avoid	104
abstract_inverted_index.based	3, 66, 94
abstract_inverted_index.field	54
abstract_inverted_index.image	87
abstract_inverted_index.maps.	74
abstract_inverted_index.novel	61
abstract_inverted_index.often	16
abstract_inverted_index.patch	109
abstract_inverted_index.these	14, 27
abstract_inverted_index.which	81
abstract_inverted_index.whole	79
abstract_inverted_index.better	119
abstract_inverted_index.caused	107
abstract_inverted_index.coding	32
abstract_inverted_index.divide	85
abstract_inverted_index.image,	80
abstract_inverted_index.method	65, 98, 117
abstract_inverted_index.paper,	44
abstract_inverted_index.signal	56
abstract_inverted_index.sparse	31
abstract_inverted_index.contain	17
abstract_inverted_index.details	102
abstract_inverted_index.feature	73
abstract_inverted_index.learned	12
abstract_inverted_index.methods	6, 15
abstract_inverted_index.patches	90
abstract_inverted_index.propose	59
abstract_inverted_index.results	112
abstract_inverted_index.several	122
abstract_inverted_index.shifted	18
abstract_inverted_index.various	40
abstract_inverted_index.working	77
abstract_inverted_index.aspects.	130
abstract_inverted_index.directly	76
abstract_inverted_index.existing	123
abstract_inverted_index.features	11
abstract_inverted_index.gradient	70
abstract_inverted_index.inspired	45
abstract_inverted_index.learning	2, 93
abstract_inverted_index.maintain	100
abstract_inverted_index.methods,	95
abstract_inverted_index.proposed	36, 97, 116
abstract_inverted_index.versions	19
abstract_inverted_index.artifacts	106
abstract_inverted_index.features.	23
abstract_inverted_index.problems,	28
abstract_inverted_index.algorithms	124
abstract_inverted_index.dictionary	1, 92
abstract_inverted_index.introduced	38
abstract_inverted_index.overlapped	89
abstract_inverted_index.successful	48
abstract_inverted_index.Traditional	0
abstract_inverted_index.demonstrate	113
abstract_inverted_index.patch-based	8
abstract_inverted_index.performance	120
abstract_inverted_index.processing,	57
abstract_inverted_index.qualitative	127
abstract_inverted_index.sparse-view	62
abstract_inverted_index.Experimental	111
abstract_inverted_index.aggregation.	110
abstract_inverted_index.applications	49
abstract_inverted_index.quantitative	129
abstract_inverted_index.applications.	41
abstract_inverted_index.convolutional	30
abstract_inverted_index.reconstruction	5, 64
abstract_inverted_index.regularization	71
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	5
sustainable_development_goals[0].id	https://metadata.un.org/sdg/4
sustainable_development_goals[0].score	0.6299999952316284
sustainable_development_goals[0].display_name	Quality Education
citation_normalized_percentile