Multi-scale Adaptive Residual Network Using Total Variation for Real Image Super-Resolution
- Resource Type
- Conference
- Authors
- Ahn, Keon-Hee; Kim, Jun-Hyuk; Choi, Jun-Ho; Lee, Jong-Seok
- Source
- 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) Consumer Electronics - Asia (ICCE-Asia), 2020 IEEE International Conference on. :1-4 Nov, 2020
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Adaptation models
Convolution
Feature extraction
Conferences
Computational modeling
Training
Adaptive systems
Single image super-resolution
real-world image super-resolution
attention mechanism
total variation
- Language
Single image super-resolution (SISR) has developed fast for recent years. Most of the SISR models are trained and evaluated with simulated data where low-resolution (LR) images are generated from high-resolution (HR) images using pre-defined degradation. In contrast, real-world image super-resolution (RealSR) is more challenging since the process of obtaining LR images is formulated by complex degradation. To solve this problem, we propose the multi-scale adaptive real image super-resolution (MARS). Our model extracts complex features in the image and uses them for upscaling adaptively. Experimental results show that the proposed method can improve the quality of the super-resolved images in RealSR.