International Journal of Engineering and Manufacturing(IJEM)

ISSN: 2305-3631 (Print), ISSN: 2306-5982 (Online)

Published By: MECS Press

IJEM Vol.13, No.2, Apr. 2023

Analysis on Image Enhancement Techniques

Full Text (PDF, 665KB), PP.9-21

Views:4   Downloads:1


Shekhar Karanwal

Index Terms

Image Enhancement Techniques (IET); Full Reference Based Image Quality Measures (FRBIQM); Pixel Difference Based Image Quality Measures (PDBIQM); Edge Based Image Quality Measures (EBIQM); Corner Based Image Quality Measures (CBIQM).


Image Enhancement is crucial phase of particular application. These enhancement techniques become essential when there is every possibility of image degradation due to uncontrolled variations. These variations are categorized into light, emotion, noise, pose, blur and corruption. The enhanced images provide better images from which feature extraction is performed more effectively. Therefore the two major objectives of the proposed work are aligned in two phases. First phase of this paper discuss about Image Enhancement Techniques (IET) for improving image intensity. Second phase provide detailed elaboration of various Full Reference Based Image Quality Measures (FRBIQM). FRBIQM is further categorized into Pixel Difference Based Image Quality Measures (PDBIQM), Edge Based Image Quality Measures (EBIQM) and Corner Based Image Quality Measures (CBIQM). First image quality measure employs different techniques to evaluate performance between original and distorted image. Second image quality measure deploy edge detection techniques, which are essential for increasing the robustness (in feature extraction) and third image quality measure discuss corner based detection techniques, which are essential for enhancing robustness (in feature extraction). All these techniques are discussed with their examples. This paper provide brief survey of IET and FRBIQM. The significance and the value of the proposed work is to select the best image enhancement techniques and image quality measures among all (described ones) for features extraction. The one which gives the best results will be used for feature extraction. 

Cite This Paper

Shekhar Karanwal, "Analysis on Image Enhancement Techniques", International Journal of Engineering and Manufacturing (IJEM), Vol.13, No.2, pp. 9-21, 2023. DOI:10.5815/ijem.2023.02.02


[1]S. Karanwal, “A comparative study of 14 state of art descriptors for face recognition”, Multimedia Tools and Applications, vol.80, 2021, pp. 12195-12234, 2021.

[2]S. Karanwal, M. Diwakar, “Two novel color local descriptors for face recognition, Optik-International Journal for Light and Electron Optics, vol. 226, 2021.

[3]S. Karanwal, “COC-LBP: ‘Complete Orthogonally Combined Local Binary Pattern for Face Recognition’, In UEMCON, 2021.

[4]S. Karanwal, “Robust Local Binary Pattern for Face Recognition in different Challenges”, Multimedia Tools and Applications, 2022.

[5]S. Karanwal, “Fusion of Two Novel Descriptors for Face Recognition in Distinct Challenges”, In ICSTSN, 2022.

[6]S. Karanwal, “Improved LBP based Descriptors in Harsh Illumination Variations for Face Recognition”, In ACIT, 2021.

[7]S. Karanwal, “Graph Based Structure Binary Pattern for Face Analysis”, Optik-International Journal for Light and Electron Optics, vol. 241, 2021.

[8]S. Karanwal, “Discriminative color descriptor by the fusion of three novel color descriptors’, Optik- International Journal for Light and Electron Optics, vol. 244, 2021. 

[9]I.E. Khadiri, Y.E. Merabet, Y. Ruichek, D. Chetverikov, R. Touahni, “O3S-MTP: Oriented star sampling structure based multi-scale ternary pattern for texture classification,” Signal Processing: Image Communication, vol. 84, 2020.

[10]M. Bansal, M. Kumar, M. Kumar, “2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors,” Multimedia Tools and Applications, vol. 80, pp. 18839-18857, 2021.

[11]Y. Jia, H. Liu, J. Hou, S. Kwong, Q. Zhang, “Semisupervised Affinity Matrix Learning via Dual-Channel Information Recovery,” IEEE Transactions on Cybernetics, pp.1-12, 2021.

[12]Zhang, W. Tsang, J. Li, P. Liu, X. Lu, X. Yu, “Face Hallucination With Finishing Touches,” IEEE Transactions on Image Processing, vol. 30, pp. 1728-1743, 2021.

[13]S. Karanwal, M. Diwakar, “Neighborhood and center difference‑based‑LBP for face recognition”, Pattern Analysis and Applications, vol. 24, pp. 741-761, 2021.

[14]S. Karanwal, M. Diwakar, “OD-LBP: Orthogonal difference Local Binary Pattern for Face Recognition”, Digital Signal Processing, vol.110, 2021.

[15]Z. Xie, L. Shi, Y. Li, “Two-Stage Fusion of Local Binary Pattern and Discrete Cosine Transform for Infrared and Visible Face Recognition,” In ICOIAISAA, pp. 967-975, 2021.

[16]R. Siddiqui, F. Shaikh, P. Sammulal, A. Lakshmi, “An Improved Method for Face Recognition with Incremental Approach in Illumination Invariant Conditions,” In ICCCPE, pp. 1145-1156, 2021.

[17]J. Galbally, S. Marcel,  J. Fierrez,  “Image Quality Assessment for fake Biometric Detection: Application to Iris, Finger & Face Recognition,” IEEE Transactions on Image Processing, vol. 23, no. 2, 2014.

[18]Avcibas, B. Sankur, K. Sayood, “Statistical evaluation of image quality measures,” Journal of Electronic Imaging, vol. 11, no. 2, pp.206–223, 2002.

[19]M. Gulame, K. R. Joshi, R.S. Kamthe, “A full reference based objective image quality Assessment,” In IJAEEE, vol. 2, no. 6, 2013.

[20]T. Arici, S. Dikbas, Y. Altunbasak, “A histogram modification framework and its application for image contrast enhancement,” IEEE Transactions on Image processing, vol. 18, no. 9, pp. 1921-1935, 2009.

[21]Z.Y. Chen, B.R. Abidi, D.L. Page, M.A. Abidi, “GLG: An automatic method for optimized image contrast enhancement-Part I: The basic method,” IEEE Transaction on Image Processing, vol. 15, no. 8. 2006.

[22]Gonzalez, R. C. and Woods, R. E., "Digital Image Processing: 2nd Ed.," Pearson Education, Inc., 2002.

[23]V.M. Patel, R. Maleh, A. C. Gilbert, R. Chellappa, “Gradient based image recovery methods from incomplete fourier measurements,” IEEE Transactions on Image Processing, vol. 21, no. 1, 2012.

[24]J.S. Lim, “Two-Dimensional Signal and Image Processing,” Englewood Cliffs, NJ, Prentice Hall, 1990.

[25]T.S. Huang, G.J. Yang, G.Y. Tang, “A fast two-dimensional median filtering algorithm,” IEEE transactions on Acoustics, Speech and Signal Processing, vol. 27, no. 1, 1979.

[26]R.M. Haralick, L.G. Shapiro, “Computer and Robot Vision,” vol. 1, Addison-Wesley, 1992.


[28]T.Q. Huynh, M. Ghanbari, “Scope of validity of PSNR in image/ video quality assessment,” Electronic Letters, vol. 44, no. 13, 2008.

[29]S. Yao, W. Lin, E. Ong, Z. Lu, “Contrast signal-to-noise ratio for image quality assessment,” In ICIP, pp. 397–400, 2005.

[30]A.M. Eskicioglu, P. S.  Fisher, “Image quality measures and their performance,” IEEE Transactions on Communications, vol. 43, no. 12, pp. 2959–2965, 1995.

[31]S.D. Wei, S.H. Lai, “Fast template matching algorithm based on normalized cross correlation with adaptive multilevel winner update,” IEEE Transactions on Image Processing, vol. 17, no. 11, 2008.

[32]M. Sezgin, B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146–165, 2004.

[33]T.Y. Zhang, C. Y. Suen, “A fast parallel algorithm for thinning digital patterns,” Communications of ACM, vol.27, no.3, pp.236-239, 1984.

[34]Trucco, Jain et al., “Edge Detection, Chapter 4 and 5,” pp. 1-29, 1982.

[35]M.G. Martini, C.T. Hewage, B. Villarini, “Image quality assessment based on edge preservation,” Signal Processing: Image Communication, vol. 27, no. 8, pp. 875–882, 2012.

[36]D. Marr, M. Hildreth, “Theory of Edge Detection,” In RSB, 1980.

[37]M. Basu, “Gaussian based edge detected methods: A survey,” IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, vol. 32, no. 3, 2002.

[38]J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 6, 1986.

[39]J. Chen, L.H. Zhou, J. Zhang, L. Dou, “Comparison and Application of Corner Detection Algorithms,” Journal of Multimedia, vol.4, 2009.

[40]C. Schimid, R. Mohr, C. Bauchage, “Evaluation of Interest Points Detectors,” International Journal of Computer Vision, vol. 37, no. 2, pp. 151–172, 2000.

[41]S. Smith, J. Brady, “SUSAN-A new approach to low level image processing,” International Journal of Computer Vision, Vol. 23, 1997.

[42]H.P. Moravec, “Towards Automatic Visual Obstacle Avoidance,” In IJCAI, pp. 584, 1977.

[43]L. Kitchen, A. Rosenfeld, “Gray-level Corner Detection,” Pattern Recognition Letters, pp. 95-102, 1982.

[44]P.R. Beaudet, “rotationally invariant image operators,” In ICPR, 1978.

[45]W. Forstner, E. Gulch, “Fast operator for detection & precise location of distinct points, corners & circular features,” In   IMCFPPD, 1987.

[46]W. Forstner, “A framework for low level feature extraction,” In EC CV, pp. 383-394, 1994.

[47]C. Tomasi, T. Kanade, “Detection and tracking of point features,” Technical Report, Carnegie Mellon University, pp. 91- 132, 1991.

[48]F. Heitger, L. Rosenthaler, R.V. D.  Heydt, E. Peterhans, O. Kuebler, “Simulation of Neural Contour Mechanism: From Simple to End-Stopped Cells,” Vision Research, vol. 32, no. 5, pp. 963–981, 1992.

[49]J. Cooper, S. Venkatesh, L. Kitchen, “Early jumpout corner detectors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 8, pp. 823–833, 1993.