- Tytuł:
- MFFNet: A multi-frequency feature extraction and fusion network for visual processing
- Autorzy:
-
Deng, Jinsheng
Zhang, Zhichao
Yin, Xiaoqing - Powiązania:
- https://bibliotekanauki.pl/articles/2173678.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
deblurring
multi-feature fusion
deep learning
attention mechanism
rozmywanie
fuzja wielu funkcji
głęboka nauka
mechanizm uwagi - Opis:
- Convolutional neural networks have achieved tremendous success in the areas of image processing and computer vision. However, they experience problems with low-frequency information such as semantic and category content and background color, and high-frequency information such as edge and structure. We propose an efficient and accurate deep learning framework called the multi-frequency feature extraction and fusion network (MFFNet) to perform image processing tasks such as deblurring. MFFNet is aided by edge and attention modules to restore high-frequency information and overcomes the multiscale parameter problem and the low-efficiency issue of recurrent architectures. It handles information from multiple paths and extracts features such as edges, colors, positions, and differences. Then, edge detectors and attention modules are aggregated into units to refine and learn knowledge, and efficient multi-learning features are fused into a final perception result. Experimental results indicate that the proposed framework achieves state-of-the-art deblurring performance on benchmark datasets.
- Źródło:
-
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e140466
0239-7528 - Pojawia się w:
- Bulletin of the Polish Academy of Sciences. Technical Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł