Next Article in Journal
Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application
Previous Article in Journal
Recent Developments in Time-Delay Systems and Their Applications
Previous Article in Special Issue
Reversible Data Hiding Using Inter-Component Prediction in Multiview Video Plus Depth
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle

Wavelet-Integrated Deep Networks for Single Image Super-Resolution

1
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
2
Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan
3
Department of Computer Science, Sukkur IBA University, Sukkur 65200, Pakistan
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 553; https://doi.org/10.3390/electronics8050553
Received: 25 April 2019 / Revised: 12 May 2019 / Accepted: 14 May 2019 / Published: 17 May 2019
  |  
PDF [8435 KB, uploaded 17 May 2019]
  |  

Abstract

We propose a scale-invariant deep neural network model based on wavelets for single image super-resolution (SISR). The wavelet approximation images and their corresponding wavelet sub-bands across all predefined scale factors are combined to form a big training data set. Then, mappings are determined between the wavelet sub-band images and their corresponding approximation images. Finally, the gradient clipping process is used to boost the training speed of the algorithm. Furthermore, stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT), due to its up-scaling property. In this way, we can preserve more information about the images. In the proposed model, the high-resolution image is recovered with detailed features, due to redundancy (across the scale) property of wavelets. Experimental results show that the proposed model outperforms state-of-the algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). View Full-Text
Keywords: wavelet analysis; deep learning; super-resolution; deep neural architecture; pattern mining; multi-scale analysis wavelet analysis; deep learning; super-resolution; deep neural architecture; pattern mining; multi-scale analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Sahito, F.; Zhiwen, P.; Ahmed, J.; Memon, R.A. Wavelet-Integrated Deep Networks for Single Image Super-Resolution. Electronics 2019, 8, 553.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top