Defocus blur detection via adaptive cross-level feature fusion and refinement-Reference-Cited by-同舟云学术

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Defocus blur detection via adaptive cross-level feature fusion and refinement

Published:2024-01-20 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Zhao Zijian,Yang Hang,Liu Peiyu,Nie Haitao,Zhang Zhongbo,Li Chunyu

Funder

Chinese Academy of Sciences-Youth Innovation Promotion Association

Hebei Provincial Key Research Projects

Key Research and Development Project of Hainan Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00371-023-03229-7.pdf

Reference45 articles.

1. Cai, R., Fang, M.: Blind image quality assessment by simulating the visual cortex. Vis. Comput. (2022). https://doi.org/10.1007/s00371-022-02614-y

2. Liu, Z., Xiang, Q., Tang, J., Wang, Y., Zhao, P.: Robust salient object detection for RGB images. Vis. Comput. 36, 1823–1835 (2020). https://doi.org/10.1007/s00371-019-01778-4

3. Zhang, X., Wang, R., Jiang, X., Wang, W., Gao, W.: Spatially variant defocus blur map estimation and deblurring from a single image. J. Vis. Commun. Image Represent. 35(Feb.), 257–264 (2016). https://doi.org/10.1016/j.jvcir.2016.01.002

4. Jiang, Z., Zhang, Z., Yu, Y., Liu, R.: Publisher Correction: Bilevel modeling investigated generative adversarial framework for image restoration. Vis. Comput. (2022). https://doi.org/10.1007/s00371-022-02713-w

5. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. (2017). https://doi.org/10.1109/TIP.2017.2662206

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SIDGAN: Efficient Multi-Module Architecture for Single Image Defocus Deblurring;Electronics;2024-06-09

2. Multi-Interactive Enhanced for Defocus Blur Estimation;IEEE Transactions on Computational Imaging;2024

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