Blind Image Super Resolution Using Deep Unsupervised Learning
The goal of single image super resolution (SISR) is to recover a high-resolution (HR) image from a low-resolution (LR) image. Deep learning based methods have recently made a remarkable performance gain in terms of both the effectiveness and efficiency for SISR. Most existing methods have to be trai...
Saved in:
Main Authors: | Kazuhiro Yamawaki, Yongqing Sun, Xian-Hua Han |
---|---|
Format: | article |
Language: | EN |
Published: |
MDPI AG
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/e69d84375f974e61a94f0ec7054d7e12 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Pixel-Level Kernel Estimation for Blind Super-Resolution
by: Jaihyun Lew, et al.
Published: (2021) -
Automatic Unsupervised Fabric Defect Detection Based on Self-Feature Comparison
by: Zhengrui Peng, et al.
Published: (2021) -
A New Full-Reference Image Quality Metric for Motion Blur Profile Characterization
by: Mohammad Abdullah-Al-Mamun, et al.
Published: (2021) -
Smart Glass System Using Deep Learning for the Blind and Visually Impaired
by: Mukhriddin Mukhiddinov, et al.
Published: (2021) -
Analysis of the Possibilities of Tire-Defect Inspection Based on Unsupervised Learning and Deep Learning
by: Ivan Kuric, et al.
Published: (2021)