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258300

A Proposed Video Super-Resolution Strategy using Wavelet Multi-Scale Convolutional Neural Networks

Article

Last updated: 22 Jan 2023

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Subjects

-

Tags

Electronics and Communications Engineering

Abstract

High-resolution images are often required and desired for most applications, as they incorporate complementary information. However, the optimal utilization of sensor technology and visual technology to improve picture pixel density is often limited and prohibitively expensive. As a result, employing an image processing method to build a high-resolution image from a low-resolution one is a costly and comprehensive option. The goal of video super-resolution is to restore intricate points and reduce the sensory effects. This research builds on the multi-frame super-resolution approach by using wavelet analysis to train convolutional neural networks (CNNs). For that purpose, the approach begins by applying wavelet decomposition on video segments for multi-scale assessment. Then, several CNNs are trained independently to approximate wavelet multi-scale characterizations. The trained CNNs do inference by regressing wavelet multi-scale characterizations from LR frames, followed by wavelet reconstruction, which produces recovered HR frames. This research presents a learning-based method for preserving fine features in low-resolution multi-frame images captured with various camera zoom lenses. The experimental findings confirm the proposed strategy for restoring difficult frames.

DOI

10.21608/bfemu.2022.258300

Keywords

Super-resolution, Convolutional Neural Networks, Wavelet Analysis, Multi-Frame, Multi-Scale Regression, Frequency Domain, Spatial Domain

Authors

First Name

M.

Last Name

Elgohary

MiddleName

A.

Affiliation

MSc Student, Department of Mechatronics Engineering, High Institute of Engineering and Technology, El-mahalla El-koubra, Egypt

Email

mohamed.elgohary2012@gmail.com

City

Elmahalla Elkoubra

Orcid

-

First Name

Fathi

Last Name

E. Abd EL-Samie

MiddleName

-

Affiliation

Professor, Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University Menouf, Egypt

Email

fathi_sayed@yahoo.com

City

-

Orcid

-

First Name

Walid

Last Name

El-Shafai

MiddleName

-

Affiliation

Assistant Professor, Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University Menouf, Egypt

Email

eng.waled.elshafai@gmail.com

City

Menouf

Orcid

-

First Name

M.

Last Name

Mohamed

MiddleName

A.

Affiliation

Editor in - chief, Dean of the College and Chairman of the Board of Directors, Faculty of Engineering, Mansoura University, professor Communications Engineering Dept., Faculty of Engineering, Mansoura University, Egypt

Email

mazim12@mans.edu.eg

City

-

Orcid

0000-0003-1899-3621

First Name

E.

Last Name

H. Abdelhay

MiddleName

-

Affiliation

Assistant Professor, Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt

Email

ehababdelhay@mans.edu.eg

City

Mansoura

Orcid

-

Volume

47

Article Issue

4

Related Issue

36118

Issue Date

2022-08-01

Receive Date

2022-03-20

Publish Date

2022-09-07

Page Start

1

Page End

10

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 47_Issue 4_Pages 1-10.pdf

PDF . 1.7MB

Link

https://bfemu.journals.ekb.eg/article_258300.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=258300

Order

1

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023