Accurate and Efficient Single Image Super-Resolution with Matrix Channel Attention Network
| Autor: | Hailong Ma, Bo Zhang, Xiangxiang Chu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Source code
Channel (digital image) Computer science business.industry media_common.quotation_subject Deep learning Matrix channel 02 engineering and technology Matrix (mathematics) Computer engineering Attention network 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence Single image business media_common |
| Zdroj: | Computer Vision – ACCV 2020 ISBN: 9783030695316 ACCV (2) |
| DOI: | 10.1007/978-3-030-69532-3_2 |
| Popis: | In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in Single Image Super-Resolution (SISR) tasks. However, these methods are usually computationally expensive, which constrains their application in mobile scenarios. In addition, most of the existing methods rarely take full advantage of the intermediate features which are helpful for restoration. To address these issues, we propose a moderate-size SISR network named matrix channel attention network (MCAN) by constructing a matrix ensemble of multi-connected channel attention blocks (MCAB). Several models of different sizes are released to meet various practical requirements. Extensive benchmark experiments show that the proposed models achieve better performance with much fewer multiply-adds and parameters (Source code is at https://github.com/macn3388/MCAN). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|