The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Rethinking Coarse-to-Fine Approach in Single Image Deblurring
Open accessAbstract
Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks. Conventional methods typically stack sub-networks with multi-scale input images and gradually improve sharpness of images from the bottom sub-network to the top sub-network, yielding inevitably high computational costs. Toward a fast and accurate deblurring network design, we revisit the coarse-to-fine strategy and present a multi-input multi-output U-net (MIMO-UNet). The MIMO-UNet has three distinct features. First, the single encoder of the MIMO-UNet takes multi-scale input images to ease the difficulty of training. Second, the single decoder of the MIMO-UNet outputs multiple deblurred images with different scales to mimic multi-cascaded U-nets using a single U-shaped network. Last, asymmetric feature fusion is introduced to merge multi-scale features in an efficient manner. Extensive experiments on the GoPro and RealBlur datasets demonstrate that the proposed network outperforms the state-of-the-art methods in terms of both accuracy and computational complexity. Source code is available for research purposes at https://github.com/chosj95/MIMO-UNet.
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
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Parallel Feature Pyramid Network for Object Detection
Kim, S.-W., Kook, H.-K., Sun, J.-Y., Kang, M.-C., & Ko, S.-J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01228-1_15 · Cited by 326 · cited 4×
“Although PFPNet was trained using the PASCAL VOC dataset [2] that contains 20 different classes, the blurry images in the GoPro dataset primarily contain only three classes among them, i.e., car, person, and potted plant.”
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MAXIM: Multi-Axis MLP for Image Processing
Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., & Li, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00568 · Cited by 616
DiffIR: Efficient Diffusion Model for Image Restoration
Xia, B., Zhang, Y., Wang, S., Wang, Y., Wu, X., Tian, Y., Yang, W., & Van Gool, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01204 · Cited by 412
Focal Network for Image Restoration
Cui, Y., Ren, W., Cao, X., & Knoll, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01195 · Cited by 281
Omni-Kernel Network for Image Restoration
Cui, Y., Ren, W., & Knoll, A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27907 · Cited by 279
Deblurring via Stochastic Refinement
Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A. G., & Milanfar, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01581 · Cited by 259
Metadata
- Authors
- Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., & Ko, S.-J.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 4621-4630
- OpenAlex
- W3202040256
- Cited by
- 798
- References
- 6
- FWCI
- 40.54
- Type
- conference paper
- Language
- en
- Open access
- green
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- Cho, S.-J., Ji, S.-W., Hong, J.-P., Jung, S.-W., & Ko, S.-J. (2021). Rethinking Coarse-to-Fine Approach in Single Image Deblurring. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv48922.2021.00460
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