Abstract
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.
Original language | English |
---|---|
Title of host publication | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1698-1702 |
Number of pages | 5 |
ISBN (Electronic) | 9789881476890 |
Publication status | Published - 2021 |
Event | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan Duration: 2021 Dec 14 → 2021 Dec 17 |
Publication series
Name | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings |
---|
Conference
Conference | 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 |
---|---|
Country/Territory | Japan |
City | Tokyo |
Period | 21/12/14 → 21/12/17 |
Bibliographical note
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.
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Signal Processing
- Instrumentation