Image enhancement is the processing of a given image so that the result is more suitable than the original image for a particular profession for future automated image processing, such as analysis, detection, segmentation and recognition. The essential target of image enhancement is to minimize noise from a digital image by keeping the intrinsic information of the image preserved. The main difficulty in image enhancement is determining the criteria for enhancement therefore; more than one image enhancement techniques are empirical and require interactive procedures to obtain satisfactory results. In this paper robust image enhancement algorithms are discussed, implemented to noisy images and compared according to their robustness. The algorithms are especially able to improve the contrast of medical images, fingerprint images and selenography images by means of software techniques. When deciding that one image has better quality than another image, quality measure metrics are needed. Otherwise comparing image quality just by visual appearance may not be objective because images could vary from person to person. That is why quantitative metrics are crucial to compare images for their qualities. In this paper Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) quality measure metrics are used to compare the image enhancement methods systematically. All the methods are validated by the performance measures with PSNR and MSE. It is believed that this paper will provide comprehensive reference source for the researchers involved in image enhancement field.
| Published in | International Journal of Psychological and Brain Sciences (Volume 2, Issue 5) |
| DOI | 10.11648/j.ijpbs.20170205.12 |
| Page(s) | 109-119 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Image Enhancement Algorithm, Histogram Matching, Histogram Equalization, Fuzzy Set Theory, Quality Measure Metrics
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APA Style
Emrah Irmak. (2017). A Comprehensive Reference Source for the Researchers Involved in Image Enhancement Field – A Review. International Journal of Psychological and Brain Sciences, 2(5), 109-119. https://doi.org/10.11648/j.ijpbs.20170205.12
ACS Style
Emrah Irmak. A Comprehensive Reference Source for the Researchers Involved in Image Enhancement Field – A Review. Int. J. Psychol. Brain Sci. 2017, 2(5), 109-119. doi: 10.11648/j.ijpbs.20170205.12
AMA Style
Emrah Irmak. A Comprehensive Reference Source for the Researchers Involved in Image Enhancement Field – A Review. Int J Psychol Brain Sci. 2017;2(5):109-119. doi: 10.11648/j.ijpbs.20170205.12
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Download@article{10.11648/j.ijpbs.20170205.12,
author = {Emrah Irmak},
title = {A Comprehensive Reference Source for the Researchers Involved in Image Enhancement Field – A Review},
journal = {International Journal of Psychological and Brain Sciences},
volume = {2},
number = {5},
pages = {109-119},
doi = {10.11648/j.ijpbs.20170205.12},
url = {https://doi.org/10.11648/j.ijpbs.20170205.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijpbs.20170205.12},
abstract = {Image enhancement is the processing of a given image so that the result is more suitable than the original image for a particular profession for future automated image processing, such as analysis, detection, segmentation and recognition. The essential target of image enhancement is to minimize noise from a digital image by keeping the intrinsic information of the image preserved. The main difficulty in image enhancement is determining the criteria for enhancement therefore; more than one image enhancement techniques are empirical and require interactive procedures to obtain satisfactory results. In this paper robust image enhancement algorithms are discussed, implemented to noisy images and compared according to their robustness. The algorithms are especially able to improve the contrast of medical images, fingerprint images and selenography images by means of software techniques. When deciding that one image has better quality than another image, quality measure metrics are needed. Otherwise comparing image quality just by visual appearance may not be objective because images could vary from person to person. That is why quantitative metrics are crucial to compare images for their qualities. In this paper Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) quality measure metrics are used to compare the image enhancement methods systematically. All the methods are validated by the performance measures with PSNR and MSE. It is believed that this paper will provide comprehensive reference source for the researchers involved in image enhancement field.},
year = {2017}
}
TY - JOUR T1 - A Comprehensive Reference Source for the Researchers Involved in Image Enhancement Field – A Review AU - Emrah Irmak Y1 - 2017/12/03 PY - 2017 N1 - https://doi.org/10.11648/j.ijpbs.20170205.12 DO - 10.11648/j.ijpbs.20170205.12 T2 - International Journal of Psychological and Brain Sciences JF - International Journal of Psychological and Brain Sciences JO - International Journal of Psychological and Brain Sciences SP - 109 EP - 119 PB - Science Publishing Group SN - 2575-1573 UR - https://doi.org/10.11648/j.ijpbs.20170205.12 AB - Image enhancement is the processing of a given image so that the result is more suitable than the original image for a particular profession for future automated image processing, such as analysis, detection, segmentation and recognition. The essential target of image enhancement is to minimize noise from a digital image by keeping the intrinsic information of the image preserved. The main difficulty in image enhancement is determining the criteria for enhancement therefore; more than one image enhancement techniques are empirical and require interactive procedures to obtain satisfactory results. In this paper robust image enhancement algorithms are discussed, implemented to noisy images and compared according to their robustness. The algorithms are especially able to improve the contrast of medical images, fingerprint images and selenography images by means of software techniques. When deciding that one image has better quality than another image, quality measure metrics are needed. Otherwise comparing image quality just by visual appearance may not be objective because images could vary from person to person. That is why quantitative metrics are crucial to compare images for their qualities. In this paper Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) quality measure metrics are used to compare the image enhancement methods systematically. All the methods are validated by the performance measures with PSNR and MSE. It is believed that this paper will provide comprehensive reference source for the researchers involved in image enhancement field. VL - 2 IS - 5 ER -
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