Image super resolution reconstruction using iterative adaptive regularization method and genetic algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Super resolution is a technique to obtain high resolution images from several degraded low-resolution images. This has got attention in the research society because of its wide use in many fields of science and technology. Even though many methods exist for super resolution, adaptive regularization method is preferred because of its simplicity and the constraints used to get better image restoration result. In this paper first adaptive algorithm is considered to restore better edge and texture of image. Further Genetic algorithm is used to smooth the noise and better frequency addition into the image to get an optimum super resolution image.

Original languageEnglish (US)
Title of host publicationComputational Intelligence in Data Mining - Proceedings of the International Conference on CIDM
PublisherSpringer Science and Business Media Deutschland GmbH
Pages675-681
Number of pages7
Volume32
ISBN (Electronic)9788132222071
DOIs
StatePublished - 2015
Event1st International Conference on Computational Intelligence in Data Mining, ICCIDM 2014 - Sambalpur, India
Duration: Dec 20 2014Dec 21 2014

Publication series

NameSmart Innovation, Systems and Technologies
Volume32
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference1st International Conference on Computational Intelligence in Data Mining, ICCIDM 2014
CountryIndia
CitySambalpur
Period12/20/1412/21/14

Fingerprint

Image resolution
Genetic algorithms
Optical resolving power
Adaptive algorithms
Image reconstruction
Textures
Genetic algorithm
Regularization

Keywords

  • (LR:HR)
  • Genetic algorithm (GA)
  • Low/high Resolution
  • Peak signal to noise ratio (PSNR)
  • Regularization

ASJC Scopus subject areas

  • Computer Science(all)
  • Decision Sciences(all)

Cite this

Panda, S. S. (2015). Image super resolution reconstruction using iterative adaptive regularization method and genetic algorithm. In Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM (Vol. 32, pp. 675-681). (Smart Innovation, Systems and Technologies; Vol. 32). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-81-322-2208-8_62

Image super resolution reconstruction using iterative adaptive regularization method and genetic algorithm. / Panda, S. S.

Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM. Vol. 32 Springer Science and Business Media Deutschland GmbH, 2015. p. 675-681 (Smart Innovation, Systems and Technologies; Vol. 32).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Panda, SS 2015, Image super resolution reconstruction using iterative adaptive regularization method and genetic algorithm. in Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM. vol. 32, Smart Innovation, Systems and Technologies, vol. 32, Springer Science and Business Media Deutschland GmbH, pp. 675-681, 1st International Conference on Computational Intelligence in Data Mining, ICCIDM 2014, Sambalpur, India, 12/20/14. https://doi.org/10.1007/978-81-322-2208-8_62
Panda SS. Image super resolution reconstruction using iterative adaptive regularization method and genetic algorithm. In Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM. Vol. 32. Springer Science and Business Media Deutschland GmbH. 2015. p. 675-681. (Smart Innovation, Systems and Technologies). https://doi.org/10.1007/978-81-322-2208-8_62
Panda, S. S. / Image super resolution reconstruction using iterative adaptive regularization method and genetic algorithm. Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM. Vol. 32 Springer Science and Business Media Deutschland GmbH, 2015. pp. 675-681 (Smart Innovation, Systems and Technologies).
@inproceedings{286863662c084cf18338a5f37ce8d76b,
title = "Image super resolution reconstruction using iterative adaptive regularization method and genetic algorithm",
abstract = "Super resolution is a technique to obtain high resolution images from several degraded low-resolution images. This has got attention in the research society because of its wide use in many fields of science and technology. Even though many methods exist for super resolution, adaptive regularization method is preferred because of its simplicity and the constraints used to get better image restoration result. In this paper first adaptive algorithm is considered to restore better edge and texture of image. Further Genetic algorithm is used to smooth the noise and better frequency addition into the image to get an optimum super resolution image.",
keywords = "(LR:HR), Genetic algorithm (GA), Low/high Resolution, Peak signal to noise ratio (PSNR), Regularization",
author = "Panda, {S. S.}",
year = "2015",
doi = "10.1007/978-81-322-2208-8_62",
language = "English (US)",
volume = "32",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "675--681",
booktitle = "Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM",
address = "Germany",

}

TY - GEN

T1 - Image super resolution reconstruction using iterative adaptive regularization method and genetic algorithm

AU - Panda, S. S.

PY - 2015

Y1 - 2015

N2 - Super resolution is a technique to obtain high resolution images from several degraded low-resolution images. This has got attention in the research society because of its wide use in many fields of science and technology. Even though many methods exist for super resolution, adaptive regularization method is preferred because of its simplicity and the constraints used to get better image restoration result. In this paper first adaptive algorithm is considered to restore better edge and texture of image. Further Genetic algorithm is used to smooth the noise and better frequency addition into the image to get an optimum super resolution image.

AB - Super resolution is a technique to obtain high resolution images from several degraded low-resolution images. This has got attention in the research society because of its wide use in many fields of science and technology. Even though many methods exist for super resolution, adaptive regularization method is preferred because of its simplicity and the constraints used to get better image restoration result. In this paper first adaptive algorithm is considered to restore better edge and texture of image. Further Genetic algorithm is used to smooth the noise and better frequency addition into the image to get an optimum super resolution image.

KW - (LR:HR)

KW - Genetic algorithm (GA)

KW - Low/high Resolution

KW - Peak signal to noise ratio (PSNR)

KW - Regularization

UR - http://www.scopus.com/inward/record.url?scp=84917707241&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84917707241&partnerID=8YFLogxK

U2 - 10.1007/978-81-322-2208-8_62

DO - 10.1007/978-81-322-2208-8_62

M3 - Conference contribution

AN - SCOPUS:84917707241

VL - 32

T3 - Smart Innovation, Systems and Technologies

SP - 675

EP - 681

BT - Computational Intelligence in Data Mining - Proceedings of the International Conference on CIDM

PB - Springer Science and Business Media Deutschland GmbH

ER -