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.2022.0130882
PDF

Novel Deep Learning Technique to Improve Resolution of Low-Quality Finger Print Image for Bigdata Applications

Author 1: Lisha P P
Author 2: Jayasree V K

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 8, 2022.

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

Abstract: High-resolution images are highly in demand when they are utilized for different analysis purposes and obviously due to their quality aesthetic visual impact. The objective of image super-resolution (SR) is to reconstruct a high-resolution (HR) image from a low-resolution (LR)image. Storing, transferring and processing of high-resolution (HR) images have got many practical issues in big data domain. In the case of finger print images, the data is huge because of the huge number of populations. So instead of transferring or storing these finger print images in its original form (HR images), it cost very low if we choose its low-resolution form. By using sampling technique, we can easily generate LR images, but the main problem is to regenerate HR image from these LR images. So, this paper addresses this problem, a novel method for enhancing resolution of low-resolution fingerprint images of size 50 x 50 to a high-resolution image of size 400 x 400 using convolutional neural network (CNN) architecture followed by sub pixel convolution operation for up sampling with no loss of promising features available in low-resolution image has been proposed. The pro-posed model contains five convolutional layers, each of which has an appropriate number of filter channels, activation functions, and optimization functions. The proposed model was trained using three publicly accessible fingerprint datasets FVC 2004 DB1, DB2, and DB3 after being validation and testing were done using 10 percent of these fingerprint data sets. In terms of performance measures like Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM) and loss functions, the quantitative and qualitative results show that the proposed model greatly outperformed existing state-of-the-art techniques like Enhanced deep residual network (EDSR), wide activation for image and video SR (WDSR), Generative adversarial network(GAN) based models and Auto-encoder-based models.

Keywords: Single image super-resolution; convolution neural network; biometric; fingerprint images

Lisha P P and Jayasree V K, “Novel Deep Learning Technique to Improve Resolution of Low-Quality Finger Print Image for Bigdata Applications” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130882

@article{P2022,
title = {Novel Deep Learning Technique to Improve Resolution of Low-Quality Finger Print Image for Bigdata Applications},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130882},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130882},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Lisha P P and Jayasree V K}
}



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

Computer Vision Conference (CVC) 2026

16-17 April 2026

  • Berlin, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2025

19-20 June 2025

  • London, United Kingdom

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2025

6-7 November 2025

  • Munich, 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