Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical Correlations

Tone mapping algorithms reproduce high dynamic range (HDR) images on low dynamic range images in the standard display devices such as LCD, CRT, projectors, and printers. In this paper, we propose a statistical clustering-based tone mapping technique that would be able to adapt the local content of a...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Sensing and imaging 2020-12, Vol.21 (1), Article 8
Hauptverfasser: Neelima, N., Kumar, Y. Ravi
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 1
container_start_page
container_title Sensing and imaging
container_volume 21
creator Neelima, N.
Kumar, Y. Ravi
description Tone mapping algorithms reproduce high dynamic range (HDR) images on low dynamic range images in the standard display devices such as LCD, CRT, projectors, and printers. In this paper, we propose a statistical clustering-based tone mapping technique that would be able to adapt the local content of an image as well as its color. At first, the HDR image is partitioned into many overlapped color patches and we disintegrate each color patch into three segments: patch mean, color variation and color structure. Then based on the color structure component, the extracted color patches are clustered into a number of clusters by k-means clustering technique. For each cluster, the statistical signal processing technique namely Hessian multi set canonical correlations (HesMCC) has been produced to ascertain the transform matrix. Moreover, the HesMCC are fundamentally utilized for performing the dimensionality reduction of patches and to form effective tone mapped images. Contrasting with the current strategies, the procedures in the proposed clustering-based strategy can better adapt image color and its local structures by exploiting the image in the worldwide repetition. Experimental results show that the running time of the proposed method is less about 88.32%, 92%, 68.9%, and 29.4%, while comparing with other existing tone mapping methods.
doi_str_mv 10.1007/s11220-020-0271-x
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2343275996</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2343275996</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-f0b7c26711a8863b774a6046004ff58939a7bf6e2ac4a7771993970dcd9aeb0a3</originalsourceid><addsrcrecordid>eNp1UF1LwzAUDaLgnP4A3wI-V5M0zV0fpX5ssCHKfA63XVozurQmLWz_3rqKPvlwuJfL-bgcQq45u-WMwV3gXAgWsSOAR_sTMuFJApFgIE5_dyXPyUUIW8aklEpNyOu6cYausG2tq-jcVh_04eBwZwv6hq4ydLHDygSaH-jchGDR0VVfdzaYjmboGmcLrGnWeG9q7GzjwiU5K7EO5upnTsn70-M6m0fLl-dFdr-MipirLipZDoVQwDnOZirOASQqJtXwWVkmszROEfJSGYGFRADg6XACtik2KZqcYTwlN6Nv65vP3oROb5veuyFSi1jGApI0VQOLj6zCNyF4U-rW2x36g-ZMfzenx-Y0OwK43g8aMWrCwB068H_O_4u-AINccDs</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2343275996</pqid></control><display><type>article</type><title>Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical Correlations</title><source>SpringerLink Journals - AutoHoldings</source><creator>Neelima, N. ; Kumar, Y. Ravi</creator><creatorcontrib>Neelima, N. ; Kumar, Y. Ravi</creatorcontrib><description>Tone mapping algorithms reproduce high dynamic range (HDR) images on low dynamic range images in the standard display devices such as LCD, CRT, projectors, and printers. In this paper, we propose a statistical clustering-based tone mapping technique that would be able to adapt the local content of an image as well as its color. At first, the HDR image is partitioned into many overlapped color patches and we disintegrate each color patch into three segments: patch mean, color variation and color structure. Then based on the color structure component, the extracted color patches are clustered into a number of clusters by k-means clustering technique. For each cluster, the statistical signal processing technique namely Hessian multi set canonical correlations (HesMCC) has been produced to ascertain the transform matrix. Moreover, the HesMCC are fundamentally utilized for performing the dimensionality reduction of patches and to form effective tone mapped images. Contrasting with the current strategies, the procedures in the proposed clustering-based strategy can better adapt image color and its local structures by exploiting the image in the worldwide repetition. Experimental results show that the running time of the proposed method is less about 88.32%, 92%, 68.9%, and 29.4%, while comparing with other existing tone mapping methods.</description><identifier>ISSN: 1557-2064</identifier><identifier>EISSN: 1557-2072</identifier><identifier>DOI: 10.1007/s11220-020-0271-x</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Cluster analysis ; Clustering ; Color ; Display devices ; Dynamic range ; Electrical Engineering ; Engineering ; Imaging ; Mapping ; Microwaves ; Original Paper ; Patches (structures) ; Projectors ; Radiology ; RF and Optical Engineering ; Signal processing ; Vector quantization</subject><ispartof>Sensing and imaging, 2020-12, Vol.21 (1), Article 8</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020</rights><rights>2020© Springer Science+Business Media, LLC, part of Springer Nature 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-f0b7c26711a8863b774a6046004ff58939a7bf6e2ac4a7771993970dcd9aeb0a3</citedby><cites>FETCH-LOGICAL-c316t-f0b7c26711a8863b774a6046004ff58939a7bf6e2ac4a7771993970dcd9aeb0a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11220-020-0271-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11220-020-0271-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids></links><search><creatorcontrib>Neelima, N.</creatorcontrib><creatorcontrib>Kumar, Y. Ravi</creatorcontrib><title>Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical Correlations</title><title>Sensing and imaging</title><addtitle>Sens Imaging</addtitle><description>Tone mapping algorithms reproduce high dynamic range (HDR) images on low dynamic range images in the standard display devices such as LCD, CRT, projectors, and printers. In this paper, we propose a statistical clustering-based tone mapping technique that would be able to adapt the local content of an image as well as its color. At first, the HDR image is partitioned into many overlapped color patches and we disintegrate each color patch into three segments: patch mean, color variation and color structure. Then based on the color structure component, the extracted color patches are clustered into a number of clusters by k-means clustering technique. For each cluster, the statistical signal processing technique namely Hessian multi set canonical correlations (HesMCC) has been produced to ascertain the transform matrix. Moreover, the HesMCC are fundamentally utilized for performing the dimensionality reduction of patches and to form effective tone mapped images. Contrasting with the current strategies, the procedures in the proposed clustering-based strategy can better adapt image color and its local structures by exploiting the image in the worldwide repetition. Experimental results show that the running time of the proposed method is less about 88.32%, 92%, 68.9%, and 29.4%, while comparing with other existing tone mapping methods.</description><subject>Algorithms</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Color</subject><subject>Display devices</subject><subject>Dynamic range</subject><subject>Electrical Engineering</subject><subject>Engineering</subject><subject>Imaging</subject><subject>Mapping</subject><subject>Microwaves</subject><subject>Original Paper</subject><subject>Patches (structures)</subject><subject>Projectors</subject><subject>Radiology</subject><subject>RF and Optical Engineering</subject><subject>Signal processing</subject><subject>Vector quantization</subject><issn>1557-2064</issn><issn>1557-2072</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1UF1LwzAUDaLgnP4A3wI-V5M0zV0fpX5ssCHKfA63XVozurQmLWz_3rqKPvlwuJfL-bgcQq45u-WMwV3gXAgWsSOAR_sTMuFJApFgIE5_dyXPyUUIW8aklEpNyOu6cYausG2tq-jcVh_04eBwZwv6hq4ydLHDygSaH-jchGDR0VVfdzaYjmboGmcLrGnWeG9q7GzjwiU5K7EO5upnTsn70-M6m0fLl-dFdr-MipirLipZDoVQwDnOZirOASQqJtXwWVkmszROEfJSGYGFRADg6XACtik2KZqcYTwlN6Nv65vP3oROb5veuyFSi1jGApI0VQOLj6zCNyF4U-rW2x36g-ZMfzenx-Y0OwK43g8aMWrCwB068H_O_4u-AINccDs</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Neelima, N.</creator><creator>Kumar, Y. Ravi</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope></search><sort><creationdate>20201201</creationdate><title>Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical Correlations</title><author>Neelima, N. ; Kumar, Y. Ravi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-f0b7c26711a8863b774a6046004ff58939a7bf6e2ac4a7771993970dcd9aeb0a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Color</topic><topic>Display devices</topic><topic>Dynamic range</topic><topic>Electrical Engineering</topic><topic>Engineering</topic><topic>Imaging</topic><topic>Mapping</topic><topic>Microwaves</topic><topic>Original Paper</topic><topic>Patches (structures)</topic><topic>Projectors</topic><topic>Radiology</topic><topic>RF and Optical Engineering</topic><topic>Signal processing</topic><topic>Vector quantization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Neelima, N.</creatorcontrib><creatorcontrib>Kumar, Y. Ravi</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><jtitle>Sensing and imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Neelima, N.</au><au>Kumar, Y. Ravi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical Correlations</atitle><jtitle>Sensing and imaging</jtitle><stitle>Sens Imaging</stitle><date>2020-12-01</date><risdate>2020</risdate><volume>21</volume><issue>1</issue><artnum>8</artnum><issn>1557-2064</issn><eissn>1557-2072</eissn><abstract>Tone mapping algorithms reproduce high dynamic range (HDR) images on low dynamic range images in the standard display devices such as LCD, CRT, projectors, and printers. In this paper, we propose a statistical clustering-based tone mapping technique that would be able to adapt the local content of an image as well as its color. At first, the HDR image is partitioned into many overlapped color patches and we disintegrate each color patch into three segments: patch mean, color variation and color structure. Then based on the color structure component, the extracted color patches are clustered into a number of clusters by k-means clustering technique. For each cluster, the statistical signal processing technique namely Hessian multi set canonical correlations (HesMCC) has been produced to ascertain the transform matrix. Moreover, the HesMCC are fundamentally utilized for performing the dimensionality reduction of patches and to form effective tone mapped images. Contrasting with the current strategies, the procedures in the proposed clustering-based strategy can better adapt image color and its local structures by exploiting the image in the worldwide repetition. Experimental results show that the running time of the proposed method is less about 88.32%, 92%, 68.9%, and 29.4%, while comparing with other existing tone mapping methods.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11220-020-0271-x</doi></addata></record>
fulltext fulltext
identifier ISSN: 1557-2064
ispartof Sensing and imaging, 2020-12, Vol.21 (1), Article 8
issn 1557-2064
1557-2072
language eng
recordid cdi_proquest_journals_2343275996
source SpringerLink Journals - AutoHoldings
subjects Algorithms
Cluster analysis
Clustering
Color
Display devices
Dynamic range
Electrical Engineering
Engineering
Imaging
Mapping
Microwaves
Original Paper
Patches (structures)
Projectors
Radiology
RF and Optical Engineering
Signal processing
Vector quantization
title Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical Correlations
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T08%3A44%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Tone%20Mapping%20High%20Dynamic%20Range%20Images%20by%20Hessian%20Multiset%20Canonical%20Correlations&rft.jtitle=Sensing%20and%20imaging&rft.au=Neelima,%20N.&rft.date=2020-12-01&rft.volume=21&rft.issue=1&rft.artnum=8&rft.issn=1557-2064&rft.eissn=1557-2072&rft_id=info:doi/10.1007/s11220-020-0271-x&rft_dat=%3Cproquest_cross%3E2343275996%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2343275996&rft_id=info:pmid/&rfr_iscdi=true