Back to articles
Articles
Volume: 33 | Article ID: art00003
Image
Under Display Camera Quad Bayer Raw Image Restoration using Deep Learning
  DOI :  10.2352/ISSN.2470-1173.2021.7.ISS-067  Published OnlineJanuary 2021
Abstract

Can a mobile camera see better through display? Under Display Camera (UDC) is the most awaited feature in mobile market in 2020 enabling more preferable user experience, however, there are technological obstacles to obtain acceptable UDC image quality. Mobile OLED panels are struggling to reach beyond 20% of light transmittance, leading to challenging capture conditions. To improve light sensitivity, some solutions use binned output losing spatial resolution. Optical diffraction of light in a panel induces contrast degradation and various visual artifacts including image ghosts, yellowish tint etc. Standard approach to address image quality issues is to improve blocks in the imaging pipeline including Image Signal Processor (ISP) and deblur block. In this work, we propose a novel approach to improve UDC image quality - we replace all blocks in UDC pipeline with all-in-one network – UDC d^Net. Proposed solution can deblur and reconstruct full resolution image directly from non-Bayer raw image, e.g. Quad Bayer, without requiring remosaic algorithm that rearranges non-Bayer to Bayer. Proposed network has a very large receptive field and can easily deal with large-scale visual artifacts including color moiré and ghosts. Experiments show significant improvement in image quality vs conventional pipeline – over 4dB in PSNR on popular benchmark - Kodak dataset.

Subject Areas :
Views 37
Downloads 10
 articleview.views 37
 articleview.downloads 10
  Cite this article 

Irina Kim, Yunseok Choi, Hayoung Ko, Dongpan Lim, Youngil Seo, Jeongguk Lee, Geunyoung Lee, Eundoo Heo, Seongwook Song, Sukhwan Lim, "Under Display Camera Quad Bayer Raw Image Restoration using Deep Learningin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging Sensors and Systems,  2021,  pp 67-1 - 67-7,  https://doi.org/10.2352/ISSN.2470-1173.2021.7.ISS-067

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2021
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane Springfield, VA 22151 USA