LogIn / RegistrationArticles for sale
- Head office address: Moscow, 123317, Moscow City, 8th Floor, Presnenskaya Embankment, 6, bldg. 2
- article@123mi.ru
- Сортировка:
- по горящим
- по цене
- по дате
#6777. Multi-focus image fusion via Joint convolutional analysis and synthesis sparse representation
| December 2026 | publication date |
| Proposal available till | 26-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Electrical and Electronic Engineering; Computer Vision and Pattern Recognition; Signal Processing; Software; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №6777.1   | Contract №6777.2 | Contract №6777.3 | Contract №6777.4 |
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Recently, various sparse representation methods have been successfully used in multi-focus image fusion. Most of them produce some spatial artifacts and blurring effects because they only consider the image local information due to the patch processing strategy. In order to reduce the spatial artifacts and blurring effects on the edge details and improve the robustness of the multi-focus image fusion, a novel fusion method based on joint convolutional analysis and synthesis (JCAS) sparse representation is presented. The JCAS model, which integrates the analysis sparse representation and the synthesis sparse representation by using convolutional operation, can effectively separate large-scale structures and fine-scale textures of a single image. First, each source image is decomposed into a base layer and a detail layer using the JCAS model. Second, a Laplacian pyramid transform method is used to fuse the base layers, and a weighted regional energy method is used to fuse the detail layers. Finally, the fused image is reconstructed by combining the fused base and detail layers. Experimental results demonstrate that the proposed method can obtain clearer edge details compared with some popular multi-focus image fusion methods, thus exhibiting state-of-the-art performance in terms of both visual quality and objective assessment.
Keywords:
Analysis sparse representation; Laplacian pyramid; Multi-focus image fusion; Synthesis sparse representation
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Recently, various sparse representation methods have been successfully used in multi-focus image fusion. Most of them produce some spatial artifacts and blurring effects because they only consider the image local information due to the patch processing strategy. In order to reduce the spatial artifacts and blurring effects on the edge details and improve the robustness of the multi-focus image fusion, a novel fusion method based on joint convolutional analysis and synthesis (JCAS) sparse representation is presented. The JCAS model, which integrates the analysis sparse representation and the synthesis sparse representation by using convolutional operation, can effectively separate large-scale structures and fine-scale textures of a single image. First, each source image is decomposed into a base layer and a detail layer using the JCAS model. Second, a Laplacian pyramid transform method is used to fuse the base layers, and a weighted regional energy method is used to fuse the detail layers. Finally, the fused image is reconstructed by combining the fused base and detail layers. Experimental results demonstrate that the proposed method can obtain clearer edge details compared with some popular multi-focus image fusion methods, thus exhibiting state-of-the-art performance in terms of both visual quality and objective assessment.
Keywords:
Analysis sparse representation; Laplacian pyramid; Multi-focus image fusion; Synthesis sparse representation
Contacts :
