Markov Random Field Modeling in Median Pyramidal Transform Domain for Denoising Applications View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2002-05

AUTHORS

Ilya Gluhovsky, Vladimir Melnik, Ilya Shmulevich

ABSTRACT

We consider a median pyramidal transform for denoising applications. Traditional techniques of pyramidal denoising are similar to those in wavelet-based methods. In order to remove noise, they use the thresholding of transform coefficients. We propose to model the structure of the transform coefficients as a Markov random field. The goal of modeling transform coefficients is to retain significant coefficients on each scale and to discard the rest. Estimation of the transform coefficient structure is obtained via a Markov chain sampler. A technique is proposed to estimate the parameters of the field's distribution. The advantage of our method is that we are able to utilize the interactions between transform coefficients, both within each scale and among the scales, which leads to denoising improvement as demonstrated by numerical simulations. More... »

PAGES

237-249

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1020381711050

DOI

http://dx.doi.org/10.1023/a:1020381711050

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034737650


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Sun Microsystems Laboratories, Palo Alto, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.419799.b", 
          "name": [
            "Sun Microsystems Laboratories, Palo Alto, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gluhovsky", 
        "givenName": "Ilya", 
        "id": "sg:person.014110201605.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014110201605.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Nokia Networks, Espoo, Finland", 
          "id": "http://www.grid.ac/institutes/grid.6533.3", 
          "name": [
            "Nokia Networks, Espoo, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Melnik", 
        "givenName": "Vladimir", 
        "id": "sg:person.010407071431.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010407071431.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cancer Genomics Laboratory, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA", 
          "id": "http://www.grid.ac/institutes/grid.240145.6", 
          "name": [
            "Cancer Genomics Laboratory, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shmulevich", 
        "givenName": "Ilya", 
        "id": "sg:person.01354314446.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354314446.15"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2002-05", 
    "datePublishedReg": "2002-05-01", 
    "description": "We consider a median pyramidal transform for denoising applications. Traditional techniques of pyramidal denoising are similar to those in wavelet-based methods. In order to remove noise, they use the thresholding of transform coefficients. We propose to model the structure of the transform coefficients as a Markov random field. The goal of modeling transform coefficients is to retain significant coefficients on each scale and to discard the rest. Estimation of the transform coefficient structure is obtained via a Markov chain sampler. A technique is proposed to estimate the parameters of the field's distribution. The advantage of our method is that we are able to utilize the interactions between transform coefficients, both within each scale and among the scales, which leads to denoising improvement as demonstrated by numerical simulations.", 
    "genre": "article", 
    "id": "sg:pub.10.1023/a:1020381711050", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1041815", 
        "issn": [
          "0924-9907", 
          "1573-7683"
        ], 
        "name": "Journal of Mathematical Imaging and Vision", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "16"
      }
    ], 
    "keywords": [
      "transform coefficients", 
      "wavelet-based method", 
      "field distribution", 
      "numerical simulations", 
      "field modeling", 
      "transform domain", 
      "random field modeling", 
      "Markov random field modeling", 
      "denoising applications", 
      "coefficient", 
      "coefficient structure", 
      "applications", 
      "traditional techniques", 
      "simulations", 
      "Markov chain sampler", 
      "structure", 
      "transform", 
      "technique", 
      "noise", 
      "denoising", 
      "method", 
      "Markov random field", 
      "modeling", 
      "distribution", 
      "random fields", 
      "estimation", 
      "parameters", 
      "sampler", 
      "significant coefficients", 
      "field", 
      "thresholding", 
      "scale", 
      "advantages", 
      "order", 
      "improvement", 
      "interaction", 
      "domain", 
      "goal", 
      "rest"
    ], 
    "name": "Markov Random Field Modeling in Median Pyramidal Transform Domain for Denoising Applications", 
    "pagination": "237-249", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1034737650"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1023/a:1020381711050"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1023/a:1020381711050", 
      "https://app.dimensions.ai/details/publication/pub.1034737650"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:22", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_354.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1023/a:1020381711050"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1023/a:1020381711050'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1023/a:1020381711050'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/a:1020381711050'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1023/a:1020381711050'


 

This table displays all metadata directly associated to this object as RDF triples.

117 TRIPLES      21 PREDICATES      65 URIs      57 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1023/a:1020381711050 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N71032e21a97c4b009edbd56c36733344
4 schema:datePublished 2002-05
5 schema:datePublishedReg 2002-05-01
6 schema:description We consider a median pyramidal transform for denoising applications. Traditional techniques of pyramidal denoising are similar to those in wavelet-based methods. In order to remove noise, they use the thresholding of transform coefficients. We propose to model the structure of the transform coefficients as a Markov random field. The goal of modeling transform coefficients is to retain significant coefficients on each scale and to discard the rest. Estimation of the transform coefficient structure is obtained via a Markov chain sampler. A technique is proposed to estimate the parameters of the field's distribution. The advantage of our method is that we are able to utilize the interactions between transform coefficients, both within each scale and among the scales, which leads to denoising improvement as demonstrated by numerical simulations.
7 schema:genre article
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf N08874f68fc7749749584e94af53b74d7
11 Nf9ff4430f3474d95bad69aeda70b0f08
12 sg:journal.1041815
13 schema:keywords Markov chain sampler
14 Markov random field
15 Markov random field modeling
16 advantages
17 applications
18 coefficient
19 coefficient structure
20 denoising
21 denoising applications
22 distribution
23 domain
24 estimation
25 field
26 field distribution
27 field modeling
28 goal
29 improvement
30 interaction
31 method
32 modeling
33 noise
34 numerical simulations
35 order
36 parameters
37 random field modeling
38 random fields
39 rest
40 sampler
41 scale
42 significant coefficients
43 simulations
44 structure
45 technique
46 thresholding
47 traditional techniques
48 transform
49 transform coefficients
50 transform domain
51 wavelet-based method
52 schema:name Markov Random Field Modeling in Median Pyramidal Transform Domain for Denoising Applications
53 schema:pagination 237-249
54 schema:productId N2510db0979a247dc8f0e3a5d662afa94
55 Ne05ee1278bc84662b6f824d812c6f18a
56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034737650
57 https://doi.org/10.1023/a:1020381711050
58 schema:sdDatePublished 2022-05-20T07:22
59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
60 schema:sdPublisher N0f30d944a12949149174b6fcebaed6c0
61 schema:url https://doi.org/10.1023/a:1020381711050
62 sgo:license sg:explorer/license/
63 sgo:sdDataset articles
64 rdf:type schema:ScholarlyArticle
65 N08874f68fc7749749584e94af53b74d7 schema:issueNumber 3
66 rdf:type schema:PublicationIssue
67 N0f30d944a12949149174b6fcebaed6c0 schema:name Springer Nature - SN SciGraph project
68 rdf:type schema:Organization
69 N2510db0979a247dc8f0e3a5d662afa94 schema:name dimensions_id
70 schema:value pub.1034737650
71 rdf:type schema:PropertyValue
72 N71032e21a97c4b009edbd56c36733344 rdf:first sg:person.014110201605.09
73 rdf:rest Nab8aca99ec3e4c0e9fa200615a356886
74 N8a5a47708fb2413686fb78832ba73e8a rdf:first sg:person.01354314446.15
75 rdf:rest rdf:nil
76 Nab8aca99ec3e4c0e9fa200615a356886 rdf:first sg:person.010407071431.44
77 rdf:rest N8a5a47708fb2413686fb78832ba73e8a
78 Ne05ee1278bc84662b6f824d812c6f18a schema:name doi
79 schema:value 10.1023/a:1020381711050
80 rdf:type schema:PropertyValue
81 Nf9ff4430f3474d95bad69aeda70b0f08 schema:volumeNumber 16
82 rdf:type schema:PublicationVolume
83 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
84 schema:name Mathematical Sciences
85 rdf:type schema:DefinedTerm
86 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
87 schema:name Statistics
88 rdf:type schema:DefinedTerm
89 sg:journal.1041815 schema:issn 0924-9907
90 1573-7683
91 schema:name Journal of Mathematical Imaging and Vision
92 schema:publisher Springer Nature
93 rdf:type schema:Periodical
94 sg:person.010407071431.44 schema:affiliation grid-institutes:grid.6533.3
95 schema:familyName Melnik
96 schema:givenName Vladimir
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010407071431.44
98 rdf:type schema:Person
99 sg:person.01354314446.15 schema:affiliation grid-institutes:grid.240145.6
100 schema:familyName Shmulevich
101 schema:givenName Ilya
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354314446.15
103 rdf:type schema:Person
104 sg:person.014110201605.09 schema:affiliation grid-institutes:grid.419799.b
105 schema:familyName Gluhovsky
106 schema:givenName Ilya
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014110201605.09
108 rdf:type schema:Person
109 grid-institutes:grid.240145.6 schema:alternateName Cancer Genomics Laboratory, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
110 schema:name Cancer Genomics Laboratory, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
111 rdf:type schema:Organization
112 grid-institutes:grid.419799.b schema:alternateName Sun Microsystems Laboratories, Palo Alto, CA, USA
113 schema:name Sun Microsystems Laboratories, Palo Alto, CA, USA
114 rdf:type schema:Organization
115 grid-institutes:grid.6533.3 schema:alternateName Nokia Networks, Espoo, Finland
116 schema:name Nokia Networks, Espoo, Finland
117 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...