Low-Resolution Image Enhancement
Location
Session 1 (Room 1302)
Session Format
Oral Presentation
Your Campus
Statesboro Campus- Henderson Library, April 20th
Academic Unit
Department of Electrical and Computer Engineering
Research Area Topic:
Engineering and Material Sciences - Electrical
Co-Presenters and Faculty Mentors or Advisors
Dr. Rami Haddad
Abstract
Low-resolution image enhancement has long been in the public’s consciousness. Television shows, movies, and other forms of fiction have long imagined improving the quality of blurry or distorted images. This bold new technology has finally become available with the help of machine learning. Generative Adversarial Networks (GANs) hold the potential to achieve that which was thought to only exist in fiction. The GANs ability to enhance image quality without affecting the cost of the image gives it a unique opportunity to benefit many industries. Law enforcement could identify criminals with better accuracy. Internet Service Providers could transmit at lower resolutions, with the signal being upscaled to the desired resolution on the client’s side, reducing latency and bandwidth. Our research focuses on improving the performance of current GAN networks, such as Super Resolution GAN (SRGAN). Further optimization can be achieved by modifying the architecture and training configuration of the network. These improvements have a high likelihood to increase the speed and quality of the image, while decreasing the cost.
Program Description
A look into Super Resolution Generative Adversarial Networks (SRGAN). An overview of the architecture, modifications made to improve the model, and uses beyond general images. How low resolution image enhancement can benefit from SRGAN and future improvements.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Presentation Type and Release Option
Presentation (Open Access)
Start Date
4-20-2022 11:00 AM
End Date
4-20-2022 12:00 PM
Recommended Citation
Moelter, Mark, "Low-Resolution Image Enhancement" (2022). GS4 Georgia Southern Student Scholars Symposium. 53.
https://digitalcommons.georgiasouthern.edu/research_symposium/2022/2022/53
Low-Resolution Image Enhancement
Session 1 (Room 1302)
Low-resolution image enhancement has long been in the public’s consciousness. Television shows, movies, and other forms of fiction have long imagined improving the quality of blurry or distorted images. This bold new technology has finally become available with the help of machine learning. Generative Adversarial Networks (GANs) hold the potential to achieve that which was thought to only exist in fiction. The GANs ability to enhance image quality without affecting the cost of the image gives it a unique opportunity to benefit many industries. Law enforcement could identify criminals with better accuracy. Internet Service Providers could transmit at lower resolutions, with the signal being upscaled to the desired resolution on the client’s side, reducing latency and bandwidth. Our research focuses on improving the performance of current GAN networks, such as Super Resolution GAN (SRGAN). Further optimization can be achieved by modifying the architecture and training configuration of the network. These improvements have a high likelihood to increase the speed and quality of the image, while decreasing the cost.
