Multi-Channel Convolutional Neural Networks for Image Super-Resolution

Author:

OHTANI Shinya1,KATO Yu1,KUROKI Nobutaka1,HIROSE Tetsuya1,NUMA Masahiro1

Affiliation:

1. Graduate School of Engineering, Kobe University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Signal Processing

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SRFLOW‐Attention: Super Resolution with Multi‐Head Attention for Effective Use of Low‐Resolution Information;IEEJ Transactions on Electrical and Electronic Engineering;2024-04-25

2. Error Bounds for Approximations Using Multichannel Deep Convolutional Neural Networks with Downsampling;Journal of Applied Mathematics;2023-05-18

3. Multi-Category Image Super-Resolution with Convolutional Neural Network and Multi-Task Learning;IEICE Transactions on Information and Systems;2021-01-01

4. Improvement of Luminance Isotropy for Convolutional Neural Networks-Based Image Super-Resolution;IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences;2020-07-01

5. Blind Quality Index for Super Resolution Reconstructed Images Using First- and Second-Order Structural Degradation;IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences;2019-11-01

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