J. Today’s Ideas - Tomorrow’s Technol.

Image Processing Techniques: A Review

Neetu Rani

KEYWORDS

Image processing, Segmentation, Threshold, Acquisition, Enhancement.

PUBLISHED DATE June 2017
PUBLISHER The Author(s) 2017. This article is published with open access at www.chitkara.edu.in/publications
ABSTRACT

In today’s scenario image processing is one of the vast growing fields. It is a method which is commonly used to improve raw images which are received from various resources. It is a kind of signal processing. This paper provides an overview of image processing methods. The main concern of this paper is to define various techniques used in different phases of image processing.

INTRODUCTION

Image processing is spreading in various fields. Image processing is a method which is commonly used to improve raw images which are received from various resources [1]. It is a technique to transform an image into digital form and implement certain actions on it, in order to create an improved image or to abstract valuable information from it. It is a kind of signal dispensation where image is an input and output is also an image or features related with image. The purpose of image processing is distributed into several groups which are given below.

Visualization: Image processing is used to identify those objects which are not detectable.

Image sharpening and restoration: In image processing, various techniques are applied on the picture to produce a better image.

Image retrieval: By image processing user can detect only that portion of the picture which is relevant to the user.

Pattern measurement: Numerous elements in an image are measured.

Image Recognition: Substances in an image are recognized.

Image processing use mathematical procedures for processing of images. Two methods used for processing of images are analog image processing and digital image processing.

Page(s) 40–49
URL http://dspace.chitkara.edu.in/jspui/bitstream/123456789/6/1/jotitt.2017.51003.pdf
ISSN Print : 2321-3906, Online : 2321-7146
DOI 10.15415/jotitt.2017.51003
CONCLUSION

Image processing is used to enhance the quality of the picture that is taken from various resources. This paper discuss various image processing methods like as image representation, segmentation, compression, acquisition, image enhancement etc. These techniques are used in numerous areas. The method that we are choosing depends upon the application area. Every technique having its own pros and cons.

REFERENCES
  • Kulkarni, P.M.,Naik,A.N.,Bhadvankar,A.P., Review Paper on Image Processing Techniques. International Journal for Scientific Research & Development, 3(10), (2015). http://dx.doi.org/10.1109/ICSensT.2012.6461695.
  • Mahmud,S.A., Mohammed, J., B., Hasan, M. S., Alzghool,M., A Survey of Digital Image Processing Techniques in Character Recognition. IJCSNS International Journal of Computer Science and Network Security ,14(3), (2014).
  • Rao, K. M.,Overview of Image Processing. Reading Images, (2006) .
  • Kaur, A. “A review paper on image segmentation and its various techniques in image processing” International Journal of Science And Research, 3(12), (2014).
  • A.A.Aly,S.b.Deris, N.Zaki, “Reserch review for digital image segmentation techniques” International Journal of Computer Science & Information Technology 3(5), (2011).
  • Yogamangalam,R., Segmentation Techniques Comparison in Image Processing. International Journal of Engineering and Technology (IJET), 5(1), (2013.
  • Kaganami,H.Beiji,Z. “Region based segmentation versus edge detection. Intelligent “, Information Hiding and Multimedia Signal Processing, 1217-1 221 , (2009).
  • Ma,M.T., and Manjunath,B.,S., “Edge flow: A framework of boundary detection and image segmentation”. IEEE Trans. Image Process., .9(8), 1375–1388(2000). http:// dx.doi.org/10.1109/CVPR.1997.609409.
  • Li,D.,Zhang,G.,Wu,Z.,Yi.L.,An Edge Embedded Marker-Based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation, IEEE Transactions 19,2781-2787(2010).
  • Shih,F.Y., Cheng.S.Adaptive mathematical morphology foredge linking. Information Sciences, 167(4), 9-21(2004).
  • Comaniciu,D.,Meer,P.,Robust Analysis Of Feature Spaces Color Image Segmentation, Proc. IEEE CVPR Conference, 750-755,(1997).
  • Baradez, M.O., McGuckin, C.P., Forraz, N., Pettengell, R., Hoppe, “A Robust and automated unimodal histogram thresholdin and potential applications Pattern Recognition, 37(6),1131–1148(2004).
  • . J. Luo, R. T. Cray, and H.C. Lee, Incorporation of derivative priors in adaptive Bayesian color image segmentation.Proc. ICIP’97, Vol. 3, pp. 58-61, Oct 26-29, 1997 Santa Barbara,CA. http://dx.doi.org/10.1109/ICIP.1998.727372
  • S.,Dhawan., A Review of Image Compression and Comparison of its Algorithms. International Journal of Electronics & Communication Technology, 2(1),(2011).
  • Wallace, G. K.,The JPEG Still Picture Compression Standard.Comm.ACM, 34(4), (1991).
  • LU,D., Weng,Q. “A survey of image classification methods and techniques for improving classification performance”, International Journal of Remote Sensing, 28(5), 823–870. http://dx.doi.org/10.1080/01431160600746456.
  • Li,P.,LI,H.,O., Fuzzy techniques in image restoration research—a survey, International Journal Of Computational Cognition, 2(2), 131–149(2004).
  • Maru,M., “Image Restoration Techniques: A Survey”, International Journal of Computer Trends and Technology ,3(12), 2014.
  • Moustakidesa,G.,Briassoulisb,D.,E. Psarakisa,E.,Dimasb,3D image acquisition and NURBS based geometry modelling of natural objects, Advances in Engineering Software, 955–969,(2000).
  • Kuriakose,B., Preena,K.,P. “A Review on 2D Image Representation Methods”, International Journal of Engineering Research & Technology (IJERT), 4(4), (2015).
  • Kaur,G., Image Enhancement and Its Techniques, A Review, International Journal of Computer Trends and Technology (IJCTT) ,3(12),(2014).