Image restoration for real-world under-display imaging

Under-display imaging technique was recently proposed to enlarge the screen-to-body ratio for full-screen devices. However, existing image restoration algorithms have difficulty generalizing to real-world under-display (UD) images, especially to images containing strong light sources. To address thi...

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Bibliographic Details
Published in:Optics express 2021-11, Vol.29 (23), p.37820-37834
Main Authors: Gao, KeMing, Chang, Meng, Jiang, Kunjun, Wang, Yaxu, Xu, Zhihai, Feng, Huajun, Li, Qi, Hu, Zengxin, Chen, YueTing
Format: Article
Language:English
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Summary:Under-display imaging technique was recently proposed to enlarge the screen-to-body ratio for full-screen devices. However, existing image restoration algorithms have difficulty generalizing to real-world under-display (UD) images, especially to images containing strong light sources. To address this issue, we propose a novel method for building a synthetic dataset (CalibPSF dataset) and introduce a two-stage neural network to solve the under-display imaging degradation problem. The CalibPSF dataset is generated using the calibrated high dynamic range point spread function (PSF) of the under-display optical system and contains various simulated light sources. The two-stage network solves the color distortion and diffraction degradation in order. We evaluate the performance of our algorithm on our captured real-world test set. Comprehensive experiments demonstrate the superiority of our method in different dynamic range scenes.
ISSN:1094-4087
1094-4087
DOI:10.1364/OE.441256