Abstract
Convolutional neural networks (CNNs) have been brought noticeable improvement on numerous image processing applications. In particular, spatial pyramid structures have been widely used, but the number of required parameters increases exponentially as the network deepens. Therefore, we propose a channel separating gate (CSG) that can determine the number of channels in convolutional layers according to the spatial level. For a given spatial level, the CSG separates the channels of feature maps for use at the smaller level. Consequently, the standard non- pooling model can be altered to an efficient pyramid structure by performing the proposed method repeatedly. For single image super-resolution (SR) and deblurring, we demonstrate that our method improves the baseline network in terms of peak signal- to-noise ratio (PSNR) and floating-point operations (FLOPs).
Original language | English |
---|---|
Title of host publication | 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 986-987 |
Number of pages | 2 |
ISBN (Electronic) | 9781665436762 |
DOIs | |
Publication status | Published - 2021 |
Event | 10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan Duration: 2021 Oct 12 → 2021 Oct 15 |
Publication series
Name | 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021 |
---|
Conference
Conference | 10th IEEE Global Conference on Consumer Electronics, GCCE 2021 |
---|---|
Country/Territory | Japan |
City | Kyoto |
Period | 21/10/12 → 21/10/15 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- convolutional neural network (CNN)
- deblurring
- pyramid structure
- super-resolution (SR)
ASJC Scopus subject areas
- Computer Science Applications
- Signal Processing
- Biomedical Engineering
- Electrical and Electronic Engineering
- Media Technology
- Instrumentation