Image fusion using online convolutional sparse coding
As signal enhancement technique, image fusion alleviates limitation single sensor in terms to information presentation and enhance visual quality. Extracting affluent features to accurately represent image is crucial for fusion. However, filters via convolutional sparse coding (CSC) have disadvantag...
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| Veröffentlicht in: | Journal of ambient intelligence and humanized computing 2023-10, Vol.14 (10), p.13559-13570 |
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| container_end_page | 13570 |
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| container_issue | 10 |
| container_start_page | 13559 |
| container_title | Journal of ambient intelligence and humanized computing |
| container_volume | 14 |
| creator | Zhang, Chengfang Zhang, Ziyou Feng, Ziliang |
| description | As signal enhancement technique, image fusion alleviates limitation single sensor in terms to information presentation and enhance visual quality. Extracting affluent features to accurately represent image is crucial for fusion. However, filters via convolutional sparse coding (CSC) have disadvantages of heavy computation cost and low representation. Superior signal representation and low spatial complexity of online convolutional sparse coding are exploited to image fusion to compensate for shortcomings of CSC. The detail and low-frequency components of source images are firstly decomposed using two-layer decomposition. Then each layers use rules to obtain fused components. Finally, fused image can be reconstructed by both high-frequency and low-frequency layers. To verify performance of proposed method, 9 infrared-visible fusion methods and 5 medical fusion methods are used as comparison experiments. The quantitative (
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) assessments confirm superiority of method. In addition, qualitative results exhibit powerful information preservation and better visualization. |
| doi_str_mv | 10.1007/s12652-022-03822-z |
| format | Article |
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Q
ABF
,
Q
E
,
Q
M
and
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Q
ABF
,
Q
E
,
Q
M
and
Q
P
) assessments confirm superiority of method. In addition, qualitative results exhibit powerful information preservation and better visualization.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computational Intelligence</subject><subject>Computer vision</subject><subject>Decomposition</subject><subject>Deep learning</subject><subject>Design</subject><subject>Dictionaries</subject><subject>Distance learning</subject><subject>Engineering</subject><subject>Image coding</subject><subject>Image enhancement</subject><subject>Image reconstruction</subject><subject>Magnetic resonance imaging</subject><subject>Mathematical models</subject><subject>Medical research</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Original Research</subject><subject>Representations</subject><subject>Robotics and Automation</subject><subject>User Interfaces and Human Computer Interaction</subject><subject>Wavelet transforms</subject><issn>1868-5137</issn><issn>1868-5145</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9UE1LAzEQDaJgqf0DnhY8r2Yyu0n2KMWPQsGLnkOaTUrLdlOTrmB_vVNX9ObAvBlm3huGx9g18FvgXN1lELIWJReUqAmPZ2wCWuqyhqo-_-1RXbJZzltOgQ0CwITVi51d-yIMeRP7grBfF7HvNr0vXOw_YjccaGG7Iu9tyqdhS5QrdhFsl_3sp07Z2-PD6_y5XL48Leb3y9IJrI4lhgp4JURwjgNYJ51bOedAq7bx9BNobDWGWhGuVCtXoZUaLUpVK-6qFqfsZry7T_F98PlgtnFI9E42ooEGZaUkEkuMLJdizskHs0-bnU2fBrg5OWRGhww5ZL4dMkcS4SjKRO7XPv2d_kf1BTjUaOU</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Zhang, Chengfang</creator><creator>Zhang, Ziyou</creator><creator>Feng, Ziliang</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20231001</creationdate><title>Image fusion using online convolutional sparse coding</title><author>Zhang, Chengfang ; Zhang, Ziyou ; Feng, Ziliang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c234z-3f410422fcc011ac6ccbccc187d9e868183d83f57d83b7d6bfd683a367570c4d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Computational Intelligence</topic><topic>Computer vision</topic><topic>Decomposition</topic><topic>Deep learning</topic><topic>Design</topic><topic>Dictionaries</topic><topic>Distance learning</topic><topic>Engineering</topic><topic>Image coding</topic><topic>Image enhancement</topic><topic>Image reconstruction</topic><topic>Magnetic resonance imaging</topic><topic>Mathematical models</topic><topic>Medical research</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Original Research</topic><topic>Representations</topic><topic>Robotics and Automation</topic><topic>User Interfaces and Human Computer Interaction</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Chengfang</creatorcontrib><creatorcontrib>Zhang, Ziyou</creatorcontrib><creatorcontrib>Feng, Ziliang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Journal of ambient intelligence and humanized computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Chengfang</au><au>Zhang, Ziyou</au><au>Feng, Ziliang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Image fusion using online convolutional sparse coding</atitle><jtitle>Journal of ambient intelligence and humanized computing</jtitle><stitle>J Ambient Intell Human Comput</stitle><date>2023-10-01</date><risdate>2023</risdate><volume>14</volume><issue>10</issue><spage>13559</spage><epage>13570</epage><pages>13559-13570</pages><issn>1868-5137</issn><eissn>1868-5145</eissn><abstract>As signal enhancement technique, image fusion alleviates limitation single sensor in terms to information presentation and enhance visual quality. Extracting affluent features to accurately represent image is crucial for fusion. However, filters via convolutional sparse coding (CSC) have disadvantages of heavy computation cost and low representation. Superior signal representation and low spatial complexity of online convolutional sparse coding are exploited to image fusion to compensate for shortcomings of CSC. The detail and low-frequency components of source images are firstly decomposed using two-layer decomposition. Then each layers use rules to obtain fused components. Finally, fused image can be reconstructed by both high-frequency and low-frequency layers. To verify performance of proposed method, 9 infrared-visible fusion methods and 5 medical fusion methods are used as comparison experiments. The quantitative (
Q
ABF
,
Q
E
,
Q
M
and
Q
P
) assessments confirm superiority of method. In addition, qualitative results exhibit powerful information preservation and better visualization.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12652-022-03822-z</doi><tpages>12</tpages></addata></record> |
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| ispartof | Journal of ambient intelligence and humanized computing, 2023-10, Vol.14 (10), p.13559-13570 |
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| language | eng |
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| subjects | Algorithms Artificial Intelligence Computational Intelligence Computer vision Decomposition Deep learning Design Dictionaries Distance learning Engineering Image coding Image enhancement Image reconstruction Magnetic resonance imaging Mathematical models Medical research Methods Neural networks Optimization Original Research Representations Robotics and Automation User Interfaces and Human Computer Interaction Wavelet transforms |
| title | Image fusion using online convolutional sparse coding |
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