Data graph classification and super-resolution reconstruction methods based on deep learning

Qiyuan Wang; Lingbao Kong; Huixin Song

doi:10.1117/12.3048240

13 November 2024 Data graph classification and super-resolution reconstruction methods based on deep learning

Qiyuan Wang, Lingbao Kong, Huixin Song

Proceedings Volume 13280, Advanced Optical Manufacturing Technologies and Applications 2024; and Fourth International Forum of Young Scientists on Advanced Optical Manufacturing (AOMTA and YSAOM 2024); 132801A (2024) https://doi.org/10.1117/12.3048240
Event: Second Conference on Advanced Optical Manufacturing Technologies and Applications & Fourth Forum of Young Scientists on Advanced Optical Manufacturing, 2024, Xi'an, China

Abstract

During the process of structured light 3D reconstruction, the resolution of images plays a crucial role in the final reconstruction outcome. In practical terms, there are primarily two methods to enhance image resolution. Firstly, improving the performance of the camera can directly provide higher-resolution images, albeit at a higher cost. Secondly, employing deep learning methods for preprocessing acquired images before using 3D reconstruction algorithms offers another viable approach. In handling image processing practically, to address issues related to hardware performance due to excessively high actual resolutions, a novel classification method is employed for distinguishing and categorizing effective regions within the images. Following the classification and identification of these effective regions, super-resolution processing is applied specifically to these areas. This approach reduces the time required for super-resolution processing and mitigates the hardware demands of deep learning.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiyuan Wang, Lingbao Kong, and Huixin Song "Data graph classification and super-resolution reconstruction methods based on deep learning", Proc. SPIE 13280, Advanced Optical Manufacturing Technologies and Applications 2024; and Fourth International Forum of Young Scientists on Advanced Optical Manufacturing (AOMTA and YSAOM 2024), 132801A (13 November 2024); https://doi.org/10.1117/12.3048240

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Deep learning

Image processing

Super resolution

Image classification

Structured light

Image resolution

Photovoltaics

Show All Keywords

Keywords/Phrases

Search In:

Publication Years