EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusion
Abstract Infrared and visible images come from different sensors, and they have their advantages and disadvantages. In order to make the fused images contain as much salience information as possible, a practical fusion method, termed EDAfuse, is proposed in this paper. In EDAfuse, the authors introd...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | IET Image Processing |
| Online Access: | https://doi.org/10.1049/ipr2.12622 |
| _version_ | 1797900719076933632 |
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| author | Cairen Nie Dongming Zhou Rencan Nie |
| author_facet | Cairen Nie Dongming Zhou Rencan Nie |
| author_sort | Cairen Nie |
| collection | DOAJ |
| description | Abstract Infrared and visible images come from different sensors, and they have their advantages and disadvantages. In order to make the fused images contain as much salience information as possible, a practical fusion method, termed EDAfuse, is proposed in this paper. In EDAfuse, the authors introduce an encoder–decoder with the atrous spatial pyramid network for infrared and visible image fusion. The authors use the encoding network which includes three convolutional neural network (CNN) layers to extract deep features from input images. Then the proposed atrous spatial pyramid model is utilized to get five different scale features. The same scale features from the two original images are fused by our fusion strategy with the attention model and information quantity model. Finally, the decoding network is utilized to reconstruct the fused image. In the training process, the authors introduce a loss function with saliency loss to improve the ability of the model for extracting salient features from original images. In the experiment process, the authors use the average values of seven metrics for 21 fused images to evaluate the proposed method and the other seven existing methods. The results show that our method has four best values and three second‐best values. The subjective assessment also demonstrates that the proposed method outperforms the state‐of‐the‐art fusion methods. |
| first_indexed | 2024-04-10T08:50:29Z |
| format | Article |
| id | doaj.art-b2819cfce0794bafa5095713b6261d88 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-10T08:50:29Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-b2819cfce0794bafa5095713b6261d882023-02-22T04:16:55ZengWileyIET Image Processing1751-96591751-96672023-01-0117113214310.1049/ipr2.12622EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusionCairen Nie0Dongming Zhou1Rencan Nie2School of Information Science and Engineering Yunnan University Kunming ChinaSchool of Information Science and Engineering Yunnan University Kunming ChinaSchool of Information Science and Engineering Yunnan University Kunming ChinaAbstract Infrared and visible images come from different sensors, and they have their advantages and disadvantages. In order to make the fused images contain as much salience information as possible, a practical fusion method, termed EDAfuse, is proposed in this paper. In EDAfuse, the authors introduce an encoder–decoder with the atrous spatial pyramid network for infrared and visible image fusion. The authors use the encoding network which includes three convolutional neural network (CNN) layers to extract deep features from input images. Then the proposed atrous spatial pyramid model is utilized to get five different scale features. The same scale features from the two original images are fused by our fusion strategy with the attention model and information quantity model. Finally, the decoding network is utilized to reconstruct the fused image. In the training process, the authors introduce a loss function with saliency loss to improve the ability of the model for extracting salient features from original images. In the experiment process, the authors use the average values of seven metrics for 21 fused images to evaluate the proposed method and the other seven existing methods. The results show that our method has four best values and three second‐best values. The subjective assessment also demonstrates that the proposed method outperforms the state‐of‐the‐art fusion methods.https://doi.org/10.1049/ipr2.12622 |
| spellingShingle | Cairen Nie Dongming Zhou Rencan Nie EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusion IET Image Processing |
| title | EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusion |
| title_full | EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusion |
| title_fullStr | EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusion |
| title_full_unstemmed | EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusion |
| title_short | EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusion |
| title_sort | edafuse a encoder decoder with atrous spatial pyramid network for infrared and visible image fusion |
| url | https://doi.org/10.1049/ipr2.12622 |
| work_keys_str_mv | AT cairennie edafuseaencoderdecoderwithatrousspatialpyramidnetworkforinfraredandvisibleimagefusion AT dongmingzhou edafuseaencoderdecoderwithatrousspatialpyramidnetworkforinfraredandvisibleimagefusion AT rencannie edafuseaencoderdecoderwithatrousspatialpyramidnetworkforinfraredandvisibleimagefusion |