The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2023.0140283
PDF

Research on Image Sharpness Enhancement Technology based on Depth Learning

Author 1: Wenbao Lan
Author 2: Chang Che

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 2, 2023.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Image technology is widely used in security, traffic, monitoring, and other social activities. However, these images carrying detailed information will have feature distortion due to various external physical factors in social ingestion, transmission, and storage, resulting in poor image quality and clarity. Resolution determines the definition of an image. Super-resolution reconstruction is the process of transforming a low-resolution picture into a high-resolution image. To enhance the image clarity, this experiment introduces the advantages and disadvantages of the Super Resolution Convolutional Network (SRCNN) and Fast Super Resolution Convolutional Neural Network (FSRCNN) model and then constructs an image super resolution method based on DSRCNN. The algorithm consists of two sub-network blocks, an enhancement block and a purification block. The model first uses two Convolutional Neural Networks (CNN) to obtain complementary low-frequency information that improves the model's learning ability; next, it employs an enhancement block to fuse the image features of two paths via residual operation and sub-pixel convolution to prevent the loss of low-resolution image information; finally, it employs a feature purification block to refine high-frequency information that more accurately represents the predicted high-quality image. It is found that the PSNR and SSIM of the DSRCNN model can reach 33.43dB and 0.9157dB, respectively.

Keywords: Super resolution convolutional network; fast super resolution convolutional neural network; dual super-resolution convolutional network; deep learning; image definition; super resolution; image enhancement

Wenbao Lan and Chang Che, “Research on Image Sharpness Enhancement Technology based on Depth Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 14(2), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140283

@article{Lan2023,
title = {Research on Image Sharpness Enhancement Technology based on Depth Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140283},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140283},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Wenbao Lan and Chang Che}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org