Comparison of Convolutional Neural Network Models for Image Super Resolution
- Authors
- Chen, Jian; Yu, Songhyun; Jeong, Je chang
- Issue Date
- Jun-2018
- Publisher
- 한국방송∙미디어공학회
- Citation
- 2018 한국방송 미디어공학회 하계학술대회, pp.63 - 66
- Indexed
- OTHER
- Journal Title
- 2018 한국방송 미디어공학회 하계학술대회
- Start Page
- 63
- End Page
- 66
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/16857
- Abstract
- Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/16857)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.