Skip to main content
SpringerLink
Log in
Book cover

Ambient Communications and Computer Systems pp 347–355Cite as

Image Enhancement: A Review

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1097))

Abstract

Image enhancement is the main function in image processing. Different image enhancement techniques exist in the literature. The goal of image enhancement technique is to improve the quality and characteristics of image in such a way that the important information of image is easily extracted. The contrast enhancement techniques are useful in various medical image modality, such as X-ray, MRI, ultrasound, PET, SPECT, etc. Enhancement process is performed on original image to improve the quality of visibility and it is applied in various domains such as spatial, frequency and fuzzy domain. By the process of enhancement of the image become more convenient than original image. The main objective of this process is to improve the quality of image in different medical imaging modality in different domain. Here, we show the importance of enhancement technique which is used in various field.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bandara, A., and P. Giragama. 2017. A retinal image enhancement technique for blood vessel segmentation algorithm. In 2017 IEEE international conference on industrial and information systems (ICIIS), 1–5, IEEE.

    Google Scholar 

  2. Bedi, S., and R. Khandelwal. 2013. Various image enhancement techniques—A critical review. International Journal of Advanced Research in Computer and Communication Engineering 2 (3).

    Google Scholar 

  3. Choudhary, R., and S. Gawade. 2016. Survey on image contrast enhancement techniques. International Journal of Innovative Studies in Sciences and Engineering Technology 2 (3): 21–25.

    Google Scholar 

  4. Jordan, K.C., M. Menolotto, N.M. Bolster, I.A. Livingstone, and M.E. Giardini. 2017. A review of feature-based retinal image analysis. Expert Review of Ophthalmology 12 (3): 207–220.

    Article  Google Scholar 

  5. Kapoor, K., and S. Arora. 2015. Colour image enhancement based on histogram equalization. Electrical & Computer Engineering: An International Journal 4 (3): 73–82.

    Google Scholar 

  6. Kaur, S., and P. Kaur. 2015. Review and analysis of various image enhancement techniques. International Journal of Computer Applications Technology and Research 4 (5): 414–418.

    Article  Google Scholar 

  7. Khidse, S., and M. Nagori. 2014. Implementation and comparison of image enhancement techniques. International Journal of Computer Applications 96 (4).

    Google Scholar 

  8. Maragos, P. 2005. Morphological filtering for image enhancement and feature detection. Analysis 19: 18

    Google Scholar 

  9. Miri, M.S., and A. Mahloojifar. 2009. A comparison study to evaluate retinal image enhancement techniques. In 2009 IEEE international conference on signal and image processing applications (ICSIPA), 90–94, IEEE.

    Google Scholar 

  10. Nancy, E., and E.S. Kaur. 2013. Comparative analysis and implementation of image enhancement techniques using matlab. International Journal of Computer Science and Mobile Computing 2 (4).

    Google Scholar 

  11. Nirmala, D. 2015. Medical image contrast enhancement techniques. Research Journal of Pharmaceutical Biologycal and Chemical Sciences 6 (3): 321–329.

    Google Scholar 

  12. Patil, V.D., and S.D. Ruikar. 2012. PCA based image enhancement in wavelet domain. International Journal of Engineering Trends and Technology 3 (1).

    Google Scholar 

  13. Singh, N.P., and R. Srivastava. 2016. Retinal blood vessels segmentation by using gumbel probability distribution function based matched filter. Computer Methods and Programs in Biomedicine 129: 40–50.

    Article  Google Scholar 

  14. Singh, N.P., and R. Srivastava. 2017. Weibull probability distribution function-based matched filter approach for retinal blood vessels segmentation. In Advances in computational intelligence, 427–437. Heidelberg: Springer.

    Google Scholar 

  15. Singh, N.P., and R. Srivastava. 2018. Extraction of retinal blood vessels by using an extended matched filter based on second derivative of gaussian. In Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 1–9.

    Google Scholar 

  16. Singh, N.P., T. Nagahma, P. Yadav, and D. Yadav. 2018. Feature based leaf identification. 2018 5th IEEE Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON), 1–7, IEEE.

    Google Scholar 

  17. Singh, S., N.P. Singh. 2019. Machine learning-based classification of good and rotten apple. In Recent trends in communication, computing, and electronics, 377–386. Heidelberg: Springer.

    Google Scholar 

  18. Singla, N., and N. Singh. 2017. Blood vessel contrast enhancement techniques for retinal images. International Journal of Advanced Research in Computer Science 8 (5).

    Google Scholar 

  19. Yadav, P., and N.P. Singh. 2019. Classification of normal and abnormal retinal images by using feature-based machine learning approach. In Recent trends in communication, computing, and electronics, 387–396. Heidelberg: Springer.

    Google Scholar 

  20. Zhang, L., W. Dong, D. Zhang, and G. Shi. 2010. Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recognition 43 (4): 1531–1549.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. M.M.M. University of Technology, Gorakhpur, India

    Prem Kumari Verma & Divakar Yadav

  2. National Institute of Technology, Hamirpur, India

    Nagendra Pratap Singh

Authors
  1. Prem Kumari Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nagendra Pratap Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Divakar Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Management, Providence University, Taichung, Taiwan

    Yu-Chen Hu

  2. Computer Science Engineering Department, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  3. Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India

    Munesh C. Trivedi

  4. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    K. K. Mishra

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Verma, P.K., Singh, N.P., Yadav, D. (2020). Image Enhancement: A Review. In: Hu, YC., Tiwari, S., Trivedi, M., Mishra, K. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 1097. Springer, Singapore. https://doi.org/10.1007/978-981-15-1518-7_29

Download citation

Publish with us

Policies and ethics

Search

Navigation