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EFFICIENT FUSION OF DEPTH INFORMATION FOR DEFOCUS DEBLURRING

DOI:
10.60864/34xr-1g03
Citation Author(s):
Submitted by:
Jucai Zhai
Last updated:
28 March 2024 - 11:15pm
Document Type:
Poster
Document Year:
2024
Event:
Presenters:
ZHAI JUCAI
Paper Code:
IVMSP-P9.8
 

Defocus deblurring is a classic problem in image restoration tasks. The formation of its defocus blur is related to depth. Recently, the use of dual-pixel sensor designed according to depth-disparity characteristics has brought great improvements to the defocus deblurring task. However, the difficulty of real-time acquisition of dual-pixel images brings difficulties to algorithm deployment. This inspires us to remove defocus blur by single image with depth information. We propose a single-image depth-enhanced defocus deblurring network, which uses a depth map estimated by the monocular depth estimation network to guide the network defocus deblurring. We design a deep information fusion unit, which greatly improves the effect of deblurring. Experiments show that on the single image defocus deblurring task, the experimental results demonstrate the superiority of our method.

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