Volume 2, Issue 5, October 2017, Page: 109-119
A Comprehensive Reference Source for the Researchers Involved in Image Enhancement Field – A Review
Emrah Irmak, Department of Biomedical Engineering, Karabuk University, Karabuk, Turkey
Received: Oct. 14, 2017;       Accepted: Oct. 25, 2017;       Published: Dec. 3, 2017
DOI: 10.11648/j.ijpbs.20170205.12      View  1709      Downloads  74
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.
Keywords
Image Enhancement Algorithm, Histogram Matching, Histogram Equalization, Fuzzy Set Theory, Quality Measure Metrics
To cite this article
Emrah Irmak, A Comprehensive Reference Source for the Researchers Involved in Image Enhancement Field – A Review, International Journal of Psychological and Brain Sciences. Vol. 2, No. 5, 2017, pp. 109-119. doi: 10.11648/j.ijpbs.20170205.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
M. M. H. Chowdhury, M. E. Islam, N. Begum, and M. A. A. Bhuiyan, “Digital image enhancement with fuzzy rule-based filtering,” 10th Int. Conf. Comput. Inf. Technol., pp. 1–3, 2007.
[2]
P. Journal, I. Society, and R. Sensing, “Theme orıented enhancement of sea surface temperature in thermal,” vol. 15, no. 2, 1987.
[3]
E. Irmak, K. Ileri, and A. Ozkahraman, “Concept and Implementation of Fuzzy Set Theory Technique for Image Enhancement Purposes,” no. 1, pp. 1–4.
[4]
K. Venkateshwarlu and M. Bala, “Image enhancement using fuzzy inference system,” Comput. Sci. Eng., vol. 60, no. 16, pp. 8–13, 2010.
[5]
G. X. Jiu, J. F. Jiao, and L. Xiang, “Image enhancement method based on fuzzy set and subdivision,” 2011 3rd Int. Conf. Aware. Sci. Technol., pp. 174–176, 2011.
[6]
M. Yasmin, M. Sharif, and S. Masood, “Brain image enhancement-A survey,” World Appl. Sci, vol. 17, no. 9, pp. 1192–1204, 2012.
[7]
K. Hasikin and N. A. M. Isa, “Enhancement of the Low Contrast Image Using Fuzzy Set Theory,” 2012 UKSim 14th Int. Conf. Comput. Model. Simul., no. March, pp. 371–376, 2012.
[8]
R. Arun, M. Nair, R. Vrinthavani, and R. Tatavarti, “An Alpha Rooting Based Hybrid Technique for Image Enhancement,” Image (IN)., no. August, 2011.
[9]
S. S. Agaian, B. Silver, and K. A. Panetta, “Transform coefficient histogram-based image enhancement algorithms using contrast entropy,” IEEE Trans. Image Process., vol. 16, no. 3, pp. 741–758, 2007.
[10]
M. Abdullah-Al-Wadud, M. Kabir, M. Akber Dewan, and O. Chae, “A Dynamic Histogram Equalization for Image Contrast Enhancement,” IEEE Trans. Consum. Electron., vol. 53, no. 2, pp. 593–600, 2007.
[11]
X. Fang, J. Liu, W. Gu, and Y. Tang, “A method to improve the image enhancement result based on image fusion,” 2011 Int. Conf. Multimed. Technol., pp. 55–58, 2011.
[12]
J. Mohan, V. Krishnaveni, and Y. Guo, “A survey on the magnetic resonance image denoising methods,” Biomed. Signal Process. Control, vol. 9, no. 1, pp. 56–69, 2014.
[13]
L. S. Chow and R. Paramesran, “Review of medical image quality assessment,” Biomed. Signal Process. Control, vol. 27, pp. 145–154, 2016.
[14]
K. Binaee and R. P. R. Hasanzadeh, “An ultrasound image enhancement method using local gradient based fuzzy similarity,” Biomed. Signal Process. Control, vol. 13, no. 1, pp. 89–101, 2014.
[15]
S. S. Suganthi and S. Ramakrishnan, “Biomedical Signal Processing and Control Anisotropic diffusion filter based edge enhancement for segmentation of breast thermogram using level sets,” Biomed. Signal Process. Control, vol. 10, pp. 128–136, 2014.
[16]
S. Anand, R. S. S. Kumari, S. Jeeva, and T. Thivya, “Directionlet transform based sharpening and enhancement of mammographic X-ray images,” Biomed. Signal Process. Control, vol. 8, no. 4, pp. 391–399, 2013.
[17]
M. B. Hossain, K. W. Lai, B. Pingguan-Murphy, Y. C. Hum, M. I. Mohd Salim, and Y. M. Liew, “Contrast enhancement of ultrasound imaging of the knee joint cartilage for early detection of knee osteoarthritis,” Biomed. Signal Process. Control, vol. 13, no. 1, pp. 157–167, 2014.
[18]
J. Jai Jaganath Babu and G. Florence Sudha, “Adaptive speckle reduction in ultrasound images using fuzzy logic on Coefficient of Variation,” Biomed. Signal Process. Control, vol. 23, pp. 93–103, 2016.
[19]
B. Deka and P. K. Bora, “Removal of correlated speckle noise using sparse and overcomplete representations,” Biomed. Signal Process. Control, vol. 8, no. 6, pp. 520–533, 2013.
[20]
V. Janani, “Infrared Image Enhancement Techniques – A Review,” Int. Conf. Curr. trend Eng. Technol., 2014.
[21]
M. S. Imtiaz, T. H. Khan, and K. Wahid, “New Color Image Enhancement Method for Endoscopic Images,” no. Icaee, pp. 19–21, 2013.
[22]
S. Gupta, V. K. Subramanian“Localized image enhancement'' @ iitk. ac. in, E. E Dept., IIT Kanpur,” Ieee, 2014.
[23]
S. H. Contreras Ortiz, T. Chiu, and M. D. Fox, “Ultrasound image enhancement: A review,” Biomed. Signal Process. Control, vol. 7, no. 5, pp. 419–428, 2012.
[24]
P. P. S. J, P. K. Rajeswari, and I. M. April, “Membership Function modification for Image Enhancement using fuzzy logic,” vol. 2, no. 2, pp. 114–118, 2013.
[25]
C. Reshmalakshmi and M. Sasikumar, “Image Contrast Enhancement using Fuzzy Technique,” no. l, pp. 861–865, 2013.
[26]
“DIP_2E_Digital Image Processing 2nd Gonzales Woods.pdf.”.
[27]
C. Vol, “Equalize The Histogram Equalization for Image enhancement,” vol. 1, no. 5, pp. 14–21, 2012.
[28]
E. Irmak, E. Erçelebi, and A. H. Ertaş, “Brain tumor detection using monomodal intensity based medical image registration and MATLAB,” Turkish J. Electr. Eng. Comput. Sci., vol. 24, pp. 2730–2746, 2016.
[29]
M. F. Al-Samaraie, “A New Enhancement Approach for Enhancing Image of Digital Cameras by Changing the Contrast,” Int. J. Adv. Sci. Technol., vol. 32, pp. 13–22, 2011.
[30]
C. Chaudhary and M. K. Patil, “Review of Image Enhancement Techniques Using Histogram,” Int. J. Appl. or Innov. Eng. Manag., vol. 2, no. 5, pp. 343–349, 2013.
[31]
P. Mohammadi, “Subjective and Objective Quality Assessment of Image: A Survey,” arXiv Prepr. arXiv, no. June, pp. 1–50, 2014.
[32]
R. L. Easton and Jr, “Fundamentals of Digital Image Processing,” no. November, 2010.
[33]
H. R. Wu and K. R. Rao, Digital Video Image Quality and Perceptual Coding. 2005.
[34]
H. R. Sheikh and A. C. Bovik, “Image information and visual quality.,” IEEE Trans. Image Process., vol. 15, no. 2, pp. 430–444, 2006.
[35]
H. L. Tan, Z. Li, Y. H. Tan, S. Rahardja, and C. Yeo, “A perceptually relevant mse-based image quality metric,” IEEE Trans. Image Process., vol. 22, no. 11, pp. 4447–4459, 2013.
[36]
P. Kaushik and Y. Sharma, “Comparison of different image enhancement techniques based upon Psnr & Mse,” Int. J. Appl. Eng. Res., vol. 7, no. 11 SUPPL., pp. 2010–2014, 2012.
[37]
C. S. Varnan, A. Jagan, J. Kaur, D. Jyoti, and D. S. Rao, “Image Quality Assessment Techniques in Spatial,” Int. J. Comput. Sci. Technol., vol. 2, no. 3, pp. 177–184, 2011.
[38]
N. Thakur and S. Devi, “A New Method for Color Image Quality Assessment,” Int. J. Comput. Appl., vol. 15, no. 2, pp. 10–17, 2011.
[39]
M. M. Kazi, A. V Mane, R. R. Manza, and K. V Kale, “Comparison of fingerprint enhancement techniques through Mean Square Error and Peak-Signal to Noise Ratio,” Int. J. Comput. Sci. Eng. {ISSN} 0975-3397, vol. 3, no. 1, pp. 266–270, 2011.
[40]
E. Irmak and A. H. Ertas, “A review of robust image enhancement algorithms and their applications” The 4th IEEE International conference on Smart Energy Grid Engineering, August 21-24, 2016.
Browse journals by subject