Rethinking Coarse-to-Fine Approach in Single Image Deblurring

Code

• github.com/chosj95/mimo-unet
  OfficialIn paperpytorch★ 443
• github.com/hw666666666666/MIMO-UNet
  mindspore★ 3
• github.com/LKLQQ/MIMO-UNet
  mindspore★ 1
• github.com/Mind23-2/MindCode-101/tree/main/MIMO-UNet
  mindspore★ 0

Abstract

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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