Rethinking Coarse-to-Fine Approach in Single Image Deblurring

Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks. Extensive experiments on the GoPro and RealBlur datasets demonstrate that the proposed network outperforms the advanced methods in terms of both accuracy and computational complexity.

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• 1 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.
• 2 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).
• 3 The MIMO-UNet has three distinct features.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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