Image moire noise removal method based on multi-scale dynamic feature aggregation
The invention discloses an image moire noise removal method based on multi-scale dynamic feature aggregation, which comprises the following steps of: respectively constructing a residual dense block, a scale feature extraction and fusion module and a main-bypass dynamic coding module, fusing the res...
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| creator | WEI ZHIYU XU RUOTAO QIU YUNZHONG LEI ZHENGHUA |
| description | The invention discloses an image moire noise removal method based on multi-scale dynamic feature aggregation, which comprises the following steps of: respectively constructing a residual dense block, a scale feature extraction and fusion module and a main-bypass dynamic coding module, fusing the residual dense block and the scale feature extraction and fusion module into a combined module, and performing jump connection on the combined module, the main-bypass dynamic coding module structure adopts residual connection, and the combination module and the main-bypass dynamic coding module are coupled according to a preset mode to form a deep convolutional neural network. The beneficial effects of the invention are that the multi-scale feature extraction and fusion module is provided for solving the problem of insufficient multi-scale feature fusion of moire noise, and the multi-scale feature extraction and fusion module is used for better utilizing the multi-scale feature information and better realizing the com |
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The beneficial effects of the invention are that the multi-scale feature extraction and fusion module is provided for solving the problem of insufficient multi-scale feature fusion of moire noise, and the multi-scale feature extraction and fusion module is used for better utilizing the multi-scale feature information and better realizing the com</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyzEKAjEQRuE0FqLeYTzAFosabGVRtBEE-2VM_o2BJLMkWcHba-EBrF7zvbm6XSI7UBSfQUl8AWVEeXGgiPoUSw8usCSJ4hSqb4rhALLvxNEbGsB1-p7sXIbj6iUt1WzgULD6daHWp-O9OzcYpUcZ2SCh9t21bfV2v9NaHzb_mA_Frze3</recordid><startdate>20230725</startdate><enddate>20230725</enddate><creator>WEI ZHIYU</creator><creator>XU RUOTAO</creator><creator>QIU YUNZHONG</creator><creator>LEI ZHENGHUA</creator><scope>EVB</scope></search><sort><creationdate>20230725</creationdate><title>Image moire noise removal method based on multi-scale dynamic feature aggregation</title><author>WEI ZHIYU ; XU RUOTAO ; QIU YUNZHONG ; LEI ZHENGHUA</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116485666A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>WEI ZHIYU</creatorcontrib><creatorcontrib>XU RUOTAO</creatorcontrib><creatorcontrib>QIU YUNZHONG</creatorcontrib><creatorcontrib>LEI ZHENGHUA</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>WEI ZHIYU</au><au>XU RUOTAO</au><au>QIU YUNZHONG</au><au>LEI ZHENGHUA</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Image moire noise removal method based on multi-scale dynamic feature aggregation</title><date>2023-07-25</date><risdate>2023</risdate><abstract>The invention discloses an image moire noise removal method based on multi-scale dynamic feature aggregation, which comprises the following steps of: respectively constructing a residual dense block, a scale feature extraction and fusion module and a main-bypass dynamic coding module, fusing the residual dense block and the scale feature extraction and fusion module into a combined module, and performing jump connection on the combined module, the main-bypass dynamic coding module structure adopts residual connection, and the combination module and the main-bypass dynamic coding module are coupled according to a preset mode to form a deep convolutional neural network. 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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
| title | Image moire noise removal method based on multi-scale dynamic feature aggregation |
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