Image super-resolution reconstruction based on feature map attention mechanism
To improve the issue of low-frequency and high-frequency components from feature maps being treated equally in existing image super-resolution reconstruction methods, the paper proposed an image super-resolution reconstruction method using attention mechanism with feature map to facilitate reconstru...
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| Veröffentlicht in: | Applied intelligence (Dordrecht, Netherlands) Netherlands), 2021-07, Vol.51 (7), p.4367-4380 |
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| container_title | Applied intelligence (Dordrecht, Netherlands) |
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| creator | Chen, Yuantao Liu, Linwu Phonevilay, Volachith Gu, Ke Xia, Runlong Xie, Jingbo Zhang, Qian Yang, Kai |
| description | To improve the issue of low-frequency and high-frequency components from feature maps being treated equally in existing image super-resolution reconstruction methods, the paper proposed an image super-resolution reconstruction method using attention mechanism with feature map to facilitate reconstruction from original low-resolution images to multi-scale super-resolution images. The proposed model consists of a feature extraction block, an information extraction block, and a reconstruction module. Firstly, the extraction block is used to extract useful features from low-resolution images, with multiple information extraction blocks being combined with the feature map attention mechanism and passed between feature channels. Secondly, the interdependence is used to adaptively adjust the channel characteristics to restore more details. Finally, the reconstruction module reforms different scales high-resolution images. The experimental results can demonstrate that the proposed method can effectively improve not only the visual effect of images but also the results on the Set5, Set14, Urban100, and Manga109. The results can demonstrate the proposed method has structurally similarity to the image reconstruction methods. Furthermore, the evaluating indicator of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) has been improved to a certain degree, while the effectiveness of using feature map attention mechanism in image super-resolution reconstruction applications is useful and effective. |
| doi_str_mv | 10.1007/s10489-020-02116-1 |
| format | Article |
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The proposed model consists of a feature extraction block, an information extraction block, and a reconstruction module. Firstly, the extraction block is used to extract useful features from low-resolution images, with multiple information extraction blocks being combined with the feature map attention mechanism and passed between feature channels. Secondly, the interdependence is used to adaptively adjust the channel characteristics to restore more details. Finally, the reconstruction module reforms different scales high-resolution images. The experimental results can demonstrate that the proposed method can effectively improve not only the visual effect of images but also the results on the Set5, Set14, Urban100, and Manga109. The results can demonstrate the proposed method has structurally similarity to the image reconstruction methods. Furthermore, the evaluating indicator of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) has been improved to a certain degree, while the effectiveness of using feature map attention mechanism in image super-resolution reconstruction applications is useful and effective.</description><identifier>ISSN: 0924-669X</identifier><identifier>EISSN: 1573-7497</identifier><identifier>DOI: 10.1007/s10489-020-02116-1</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Artificial Intelligence ; Computer Science ; Deep learning ; Feature extraction ; Feature maps ; Image reconstruction ; Image resolution ; Image restoration ; Information retrieval ; Machines ; Manufacturing ; Mapping ; Mechanical Engineering ; Modules ; Processes ; Signal to noise ratio ; Similarity ; Visual effects</subject><ispartof>Applied intelligence (Dordrecht, Netherlands), 2021-07, Vol.51 (7), p.4367-4380</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-d56446f1cfaa0d17fb5843d66ab96bac0edcffd69d4ef3ed7ec3c1cb355bcbb73</citedby><cites>FETCH-LOGICAL-c319t-d56446f1cfaa0d17fb5843d66ab96bac0edcffd69d4ef3ed7ec3c1cb355bcbb73</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/s10489-020-02116-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10489-020-02116-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Chen, Yuantao</creatorcontrib><creatorcontrib>Liu, Linwu</creatorcontrib><creatorcontrib>Phonevilay, Volachith</creatorcontrib><creatorcontrib>Gu, Ke</creatorcontrib><creatorcontrib>Xia, Runlong</creatorcontrib><creatorcontrib>Xie, Jingbo</creatorcontrib><creatorcontrib>Zhang, Qian</creatorcontrib><creatorcontrib>Yang, Kai</creatorcontrib><title>Image super-resolution reconstruction based on feature map attention mechanism</title><title>Applied intelligence (Dordrecht, Netherlands)</title><addtitle>Appl Intell</addtitle><description>To improve the issue of low-frequency and high-frequency components from feature maps being treated equally in existing image super-resolution reconstruction methods, the paper proposed an image super-resolution reconstruction method using attention mechanism with feature map to facilitate reconstruction from original low-resolution images to multi-scale super-resolution images. The proposed model consists of a feature extraction block, an information extraction block, and a reconstruction module. Firstly, the extraction block is used to extract useful features from low-resolution images, with multiple information extraction blocks being combined with the feature map attention mechanism and passed between feature channels. Secondly, the interdependence is used to adaptively adjust the channel characteristics to restore more details. Finally, the reconstruction module reforms different scales high-resolution images. The experimental results can demonstrate that the proposed method can effectively improve not only the visual effect of images but also the results on the Set5, Set14, Urban100, and Manga109. The results can demonstrate the proposed method has structurally similarity to the image reconstruction methods. Furthermore, the evaluating indicator of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) has been improved to a certain degree, while the effectiveness of using feature map attention mechanism in image super-resolution reconstruction applications is useful and effective.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computer Science</subject><subject>Deep learning</subject><subject>Feature extraction</subject><subject>Feature maps</subject><subject>Image reconstruction</subject><subject>Image resolution</subject><subject>Image restoration</subject><subject>Information retrieval</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Mapping</subject><subject>Mechanical Engineering</subject><subject>Modules</subject><subject>Processes</subject><subject>Signal to noise ratio</subject><subject>Similarity</subject><subject>Visual 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super-resolution reconstruction based on feature map attention mechanism</title><author>Chen, Yuantao ; Liu, Linwu ; Phonevilay, Volachith ; Gu, Ke ; Xia, Runlong ; Xie, Jingbo ; Zhang, Qian ; Yang, Kai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-d56446f1cfaa0d17fb5843d66ab96bac0edcffd69d4ef3ed7ec3c1cb355bcbb73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Computer Science</topic><topic>Deep learning</topic><topic>Feature extraction</topic><topic>Feature maps</topic><topic>Image reconstruction</topic><topic>Image resolution</topic><topic>Image restoration</topic><topic>Information retrieval</topic><topic>Machines</topic><topic>Manufacturing</topic><topic>Mapping</topic><topic>Mechanical Engineering</topic><topic>Modules</topic><topic>Processes</topic><topic>Signal to noise ratio</topic><topic>Similarity</topic><topic>Visual effects</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Yuantao</creatorcontrib><creatorcontrib>Liu, Linwu</creatorcontrib><creatorcontrib>Phonevilay, Volachith</creatorcontrib><creatorcontrib>Gu, Ke</creatorcontrib><creatorcontrib>Xia, Runlong</creatorcontrib><creatorcontrib>Xie, Jingbo</creatorcontrib><creatorcontrib>Zhang, Qian</creatorcontrib><creatorcontrib>Yang, Kai</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research 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The proposed model consists of a feature extraction block, an information extraction block, and a reconstruction module. Firstly, the extraction block is used to extract useful features from low-resolution images, with multiple information extraction blocks being combined with the feature map attention mechanism and passed between feature channels. Secondly, the interdependence is used to adaptively adjust the channel characteristics to restore more details. Finally, the reconstruction module reforms different scales high-resolution images. The experimental results can demonstrate that the proposed method can effectively improve not only the visual effect of images but also the results on the Set5, Set14, Urban100, and Manga109. The results can demonstrate the proposed method has structurally similarity to the image reconstruction methods. Furthermore, the evaluating indicator of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) has been improved to a certain degree, while the effectiveness of using feature map attention mechanism in image super-resolution reconstruction applications is useful and effective.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10489-020-02116-1</doi><tpages>14</tpages></addata></record> |
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| subjects | Algorithms Artificial Intelligence Computer Science Deep learning Feature extraction Feature maps Image reconstruction Image resolution Image restoration Information retrieval Machines Manufacturing Mapping Mechanical Engineering Modules Processes Signal to noise ratio Similarity Visual effects |
| title | Image super-resolution reconstruction based on feature map attention mechanism |
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