TY - GEN
T1 - Super-Resolution Imaging Using a Focus Pixel Sensor
AU - Woo, Sung Min
AU - Ha, Jeong Won
AU - Kim, Jong Ok
N1 - Funding Information:
This work is supported by the National Research Foundation of Korea(NRF) grant funded by the Koreagovernment (MSIT) (No. 2019R1A2C1005834) and the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2020-0-01749) supervised by the IITP(Institute of Information & Communications TechnologyPlanning & Evaluation)
Publisher Copyright:
© 2021 APSIPA.
PY - 2021
Y1 - 2021
N2 - Modern camera sensors are equipped with a focus pixel, a special type of pixel that can collect separate light rays from the left (L) and right (R) directions. The phase difference between the corresponding L/R pixels is utilized to facilitate quick auto-focusing. In this study, we expand the usability of the special pixels to super-resolve an image. We design a neural net to best fuse multiple low-resolution focus pixel images with a normal image based on repetitive channel and spatial attention layer structures. Empirical results show that focus pixel images contribute to the creation of fine details by providing additional information to super-resolve an image, especially for ured areas and that the proposed neural net-based method enhances the state-of-the-art super-resolution methods that do not use focus pixels in quantitative and qualitative measures.
AB - Modern camera sensors are equipped with a focus pixel, a special type of pixel that can collect separate light rays from the left (L) and right (R) directions. The phase difference between the corresponding L/R pixels is utilized to facilitate quick auto-focusing. In this study, we expand the usability of the special pixels to super-resolve an image. We design a neural net to best fuse multiple low-resolution focus pixel images with a normal image based on repetitive channel and spatial attention layer structures. Empirical results show that focus pixel images contribute to the creation of fine details by providing additional information to super-resolve an image, especially for ured areas and that the proposed neural net-based method enhances the state-of-the-art super-resolution methods that do not use focus pixels in quantitative and qualitative measures.
UR - http://www.scopus.com/inward/record.url?scp=85126693112&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85126693112
T3 - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
SP - 1698
EP - 1702
BT - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Y2 - 14 December 2021 through 17 December 2021
ER -