Matches in SemOpenAlex for { <https://semopenalex.org/work/W4303627687> ?p ?o ?g. }
- W4303627687 endingPage "3140" @default.
- W4303627687 startingPage "3140" @default.
- W4303627687 abstract "This paper presents an image fusion network based on a special residual network and attention mechanism. Compared with the traditional fusion network, the image fusion network has the advantages of an end-to-end network and integrates the feature extraction advantages of the attention mechanism residual network. It overcomes the shortcomings of the traditional network that need complex design rules and manual operation. In this method, hierarchical feature fusion is used to achieve effective fusion. A combined loss function is designed to optimize training results and improve image fusion quality. This paper uses many qualitative and quantitative experimental analyses on different data sets. The results show that, compared with the comparison algorithm, the method in this paper has a stronger retention ability of infrared and visible light information and better indexes. 72% of eleven indexes compared with some images in the public TNO data set are optimal or sub-optimal, and 80% are optimal or suboptimal in the RoadScene data set, which is much higher than other algorithms. The overall fusion effect is more in line with human visual perception." @default.
- W4303627687 created "2022-10-08" @default.
- W4303627687 creator A5007508021 @default.
- W4303627687 creator A5018910386 @default.
- W4303627687 creator A5021994123 @default.
- W4303627687 creator A5035842819 @default.
- W4303627687 creator A5064842058 @default.
- W4303627687 creator A5077319251 @default.
- W4303627687 date "2022-09-30" @default.
- W4303627687 modified "2023-10-09" @default.
- W4303627687 title "An Image Fusion Method Based on Special Residual Network and Efficient Channel Attention" @default.
- W4303627687 cites W1563797521 @default.
- W4303627687 cites W1708141795 @default.
- W4303627687 cites W1964641132 @default.
- W4303627687 cites W1969299043 @default.
- W4303627687 cites W1980382026 @default.
- W4303627687 cites W1990250903 @default.
- W4303627687 cites W1997596006 @default.
- W4303627687 cites W1999034606 @default.
- W4303627687 cites W2009758395 @default.
- W4303627687 cites W2040833130 @default.
- W4303627687 cites W2046119925 @default.
- W4303627687 cites W2074907730 @default.
- W4303627687 cites W2091484864 @default.
- W4303627687 cites W2094162745 @default.
- W4303627687 cites W2133135191 @default.
- W4303627687 cites W2153777140 @default.
- W4303627687 cites W2161516371 @default.
- W4303627687 cites W2266694576 @default.
- W4303627687 cites W2532801510 @default.
- W4303627687 cites W2559870345 @default.
- W4303627687 cites W2562637781 @default.
- W4303627687 cites W2576508765 @default.
- W4303627687 cites W2624240493 @default.
- W4303627687 cites W2772136803 @default.
- W4303627687 cites W2806865914 @default.
- W4303627687 cites W2809216229 @default.
- W4303627687 cites W2912147220 @default.
- W4303627687 cites W2963530785 @default.
- W4303627687 cites W2963787388 @default.
- W4303627687 cites W2998012573 @default.
- W4303627687 cites W3011768656 @default.
- W4303627687 cites W3034552520 @default.
- W4303627687 cites W3102411220 @default.
- W4303627687 cites W3105639468 @default.
- W4303627687 cites W3107998196 @default.
- W4303627687 cites W3133700567 @default.
- W4303627687 cites W3143068962 @default.
- W4303627687 cites W3158080681 @default.
- W4303627687 cites W4206713196 @default.
- W4303627687 cites W4226178544 @default.
- W4303627687 doi "https://doi.org/10.3390/electronics11193140" @default.
- W4303627687 hasPublicationYear "2022" @default.
- W4303627687 type Work @default.
- W4303627687 citedByCount "2" @default.
- W4303627687 countsByYear W43036276872023 @default.
- W4303627687 crossrefType "journal-article" @default.
- W4303627687 hasAuthorship W4303627687A5007508021 @default.
- W4303627687 hasAuthorship W4303627687A5018910386 @default.
- W4303627687 hasAuthorship W4303627687A5021994123 @default.
- W4303627687 hasAuthorship W4303627687A5035842819 @default.
- W4303627687 hasAuthorship W4303627687A5064842058 @default.
- W4303627687 hasAuthorship W4303627687A5077319251 @default.
- W4303627687 hasBestOaLocation W43036276871 @default.
- W4303627687 hasConcept C11413529 @default.
- W4303627687 hasConcept C115961682 @default.
- W4303627687 hasConcept C119857082 @default.
- W4303627687 hasConcept C124101348 @default.
- W4303627687 hasConcept C138885662 @default.
- W4303627687 hasConcept C153180895 @default.
- W4303627687 hasConcept C154945302 @default.
- W4303627687 hasConcept C155512373 @default.
- W4303627687 hasConcept C158525013 @default.
- W4303627687 hasConcept C177264268 @default.
- W4303627687 hasConcept C199360897 @default.
- W4303627687 hasConcept C2776401178 @default.
- W4303627687 hasConcept C33954974 @default.
- W4303627687 hasConcept C41008148 @default.
- W4303627687 hasConcept C41895202 @default.
- W4303627687 hasConcept C50644808 @default.
- W4303627687 hasConcept C69744172 @default.
- W4303627687 hasConceptScore W4303627687C11413529 @default.
- W4303627687 hasConceptScore W4303627687C115961682 @default.
- W4303627687 hasConceptScore W4303627687C119857082 @default.
- W4303627687 hasConceptScore W4303627687C124101348 @default.
- W4303627687 hasConceptScore W4303627687C138885662 @default.
- W4303627687 hasConceptScore W4303627687C153180895 @default.
- W4303627687 hasConceptScore W4303627687C154945302 @default.
- W4303627687 hasConceptScore W4303627687C155512373 @default.
- W4303627687 hasConceptScore W4303627687C158525013 @default.
- W4303627687 hasConceptScore W4303627687C177264268 @default.
- W4303627687 hasConceptScore W4303627687C199360897 @default.
- W4303627687 hasConceptScore W4303627687C2776401178 @default.
- W4303627687 hasConceptScore W4303627687C33954974 @default.
- W4303627687 hasConceptScore W4303627687C41008148 @default.
- W4303627687 hasConceptScore W4303627687C41895202 @default.
- W4303627687 hasConceptScore W4303627687C50644808 @default.
- W4303627687 hasConceptScore W4303627687C69744172 @default.