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.
|Title of host publication
|Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2021
|18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada
Duration: 2021 Oct 11 → 2021 Oct 17
|Proceedings of the IEEE International Conference on Computer Vision
|18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
|21/10/11 → 21/10/17
Bibliographical noteFunding Information:
This work was supported by Samsung Electronics Co., Ltd (IO201210-08026-01)
© 2021 IEEE
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition