Wavelet Based Image Analysis:A Comprehensive Survey

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-21 Number-3
Year of Publication : 2015
Authors : Renjini L, Jyothi R L


Renjini L, Jyothi R L"Wavelet Based Image Analysis:A Comprehensive Survey". International Journal of Computer Trends and Technology (IJCTT) V21(3):134-140, March 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Wavelet theory is one the greatest achievement of last decade. Wavelet theory has gained popularity in solving difficult problems in mathematics, engineering etc. It can be employed in lots of fields and applications, such as signal processing, image analysis, communication systems, time frequency analysis, image compression, smoothing and image denoising , pattern recognition, finger print verification, DNA analysis, computer graphics etc. The results produced by wavelet based analysis have really astonished the modern research communities in various fields. Wavelet based analysis is still an active research area due to its tremendous applications. This paper provides basic concepts of wavelet transforms and brief idea of recent published works dealing with applications of wavelet theories.

[1] Bouden Toufik and Nibouche Mokhtar, “The Wavelet Transform for Image Processing Applications”, Intech, April 2012.
[2]M. Mozammel Hoque Chowdhury and Amina Khatun, “Image Compression Using Discrete Wavelet Transform”, International Journal of Computer Science, Volume 9, Issue 4,July, 2012, 327- 330.
[3] Dipalee Gupta and Siddhartha Choubey, “Discrete Wavelet Transform for Image Processing”, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 3, March 2015, 598-602.
[4]Meenakshi Chaudhary and Anupma Dhamija,” A Brief Study of Various Wavelet Families and Compression Techniques”, Journal of Global Research in Computer Science, Volume 4, Issue 4, April 2013, 43-49.
[5]Gagandeep Kour and Sharad P. Singh, “Image Decomposition Using Wavelet Transform”, International Journal Of Engineering and Computer Science, Volume 2, Issue 12, December 2013, 3477-3480.
[6]Vinita Arun Chaudhari and Shrikant Lade, “A Review on Image Denoising Techniques Using Wavelet Transform Methods”, International Journal of Recent Development in Engineering and Technology, Volume 3, Issue 4, October 2014, 37-41.
[7]Souparnika Jadhav, “Image Fusion Based On Wavelet Transform”, International Journal of Engineering Research, Volume 3, Issue 7, July 2014, 442-445.
[8]Rudra Pratap Singh Chauhan, Rajiva Dwivedi and Sandeep Negi, “Comparative evaluation of DWT and DT-CWT for image fusion and denoising”, International Journal of Applied Information Systems, Volume 4, Issue 2, September 2012, 40-45.
[9]Sandipan P Narote, Abhilasha S Narote and Laxman M Waghmare, “Iris Based Recognition System Using Wavelet Transform”, International Journal of Computer Science and Network Security, Volume 9, Issue 11, November 2009, 101-104.
[10] B.V. Dhandra, Shashikala Parameshwarapa and Gururaj Mukarambi, “Kannada Handwritten Vowels Recognition based on Normalized Chain Code and Wavelet Filters”, International Journal of Computer Applications Recent Advances in Information Technology, NCRAIT - November 4, 2014, 21-24.
[11] ] Xian Zhao, Ping Xiao, “Wavelet-Based The Character Recognition In MAP”, International Conference on Integrated System for Spatial Data Production Commission II, Volume 24 , PART 2, Aug.20-23, 2002, 605- 608.
[12] George S Kapogiannopoulos and Manos Papadakis, “Character recognition using a biorthogonal discrete wavelet transform “, Wavelet Applications in Signal and Image Processing IV, Department of Informatics University of Athens, Hellas (Greece), and Conference ,Volume 2825, October 23, 1996, 384-394.
[13]Suzete E. N. Correia and Joao M de Carvalho, “Optimizing the Recognition Rates of Unconstrained Handwritten Numerals Using Biorthogonal Spline Wavelets”, Pattern Recognition, 15th International Conference on Barcelona, Published by IEEE, vol.2, 2000, 251-254.
[14]Punamchand M. Mahajan, Satish R. Kolhe and Pradeep M. Patil, “Classification of Texture Images Using Multiresolution Transform”, International Journal of Advanced Research in Computer and Communication Engineering, Volume 2, Issue 8, August 2013, 3171- 3175.
[15]Marcin Kociolek, Andrzej Materka, Michal Strzelecki and Piotr Szczypinski, “Discrete Wavelet Transform - Derived Features for Digital Image Texture Analysis”, International Conference on Signal and Electronic Systems, September 2001, 163-168.
[16] Rudra Pratap Singh Chauhan, Rajiva Dwivedi and Rajesh Bhagat, “Comparative Analysis of Discrete Wavelet Transform and Complex Wavelet Transform For Image Fusion and De- Noising”, International Journal Of Engineering Science Invention, Volume 2, Issue 3, March 2013, 18-27.
[17] Joohyun Lim, Youngouk Kim and Joonki Paik, “Comparative Analysis of Wavelet-Based Scale-Invariant Feature Extraction Using Different Wavelet Bases”, International Journal of Signal Processing, Image Processing and Pattern Recognition Communications in Computer and Information Science ,Volume 61, 2009, 297-303.
[18]K.P Soman and K .I Ramachandran, “Insight into Wavelets from Theory to Practice”, 3rd Ed, Prentice- Hall Of India, New Delhi, 2010.
[19] Manpreet Kaur and Gagndeep Kaur AP, “A Survey on Implementation of Discrete Wavelet Transform for Image Denoising”, International Journal of Communications Networking System, Volume 2, Issue 1, June 2013, 158-163.
[20]Ayra Panganiban, Noel Linsangan and Felicito Caluyo, “Wavelet based Feature Extraction Algorithm for an Iris Recognition System”, Journal of Information Processing System, volume 7, Issue 3, September 2011, and 425-434.
[21]P.S Hiremath, S. Shivashankar and Jagadeesh Pujari, “Wavelet Based Features For Color Texture Classification with Application To CBIR”, International Journal of Computer Science and Network security, Volume 6, Issue 9, September 2006, 124-133.
[22] R C Gonzalez and R E Wood, “Digital Image Processing,”, Addison- Wesley, New York, 1992.

Dual Tree Complex Wavelet, Image compression, Image denoise, Multiresolution analysis, Zero crossing.