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...
Gespeichert in:
Veröffentlicht in: | Sensing and imaging 2020-12, Vol.21 (1), Article 8 |
---|---|
Hauptverfasser: | , |
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 & 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 |