Super-resolution of compressed image by deep residual network
- Authors
- Jin, Yan; Park, Bumjun; Jeong, Je chang
- Issue Date
- Nov-2018
- Publisher
- 한국방송∙미디어공학회
- Citation
- 2018 한국방송 미디어공학회 추계학술대회, pp.59 - 61
- Indexed
- OTHER
- Journal Title
- 2018 한국방송 미디어공학회 추계학술대회
- Start Page
- 59
- End Page
- 61
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/15892
- Abstract
- Highly compressed images typically not only have low resolution, but are also affected by compression artifacts. Performing image super-resolution (SR) directly on highly compressed image would simultaneously magnify the blocking artifacts. In this paper, a SR method based on deep learning is proposed. The method is an end-to-end trainable deep convolutional neural network which performs SR on compressed images so as to reduce compression artifacts and improve image resolution. The proposed network is divided into compression artifacts removal (CAR) part and SR reconstruction part, and the network is trained by three-step training method to optimize training procedure. Experiments on JPEG compressed images with quality factors of 10, 20, and 30 demonstrate the effectiveness of the proposed method on commonly used test images and image sets.
- 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/15892)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.