An Adaptive Image Enhancement using Wiener Filtering with Compression and Segmentation

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IJCA Proceedings on National Conference on Research Issues in Image Analysis and Mining Intelligence
© 2015 by IJCA Journal
NCRIIAMI 2015 - Number 1
Year of Publication: 2015
Authors:
Raajan. P
Muthuselvi. S
Agnes Saleema. A

Raajan.p, Muthuselvi.s and Agnes Saleema. A. Article: An Adaptive Image Enhancement using Wiener Filtering with Compression and Segmentation. IJCA Proceedings on National Conference on Research Issues in Image Analysis and Mining Intelligence NCRIIAMI 2015(1):15-19, June 2015. Full text available. BibTeX

@article{key:article,
	author = {Raajan.p and Muthuselvi.s and Agnes Saleema. A},
	title = {Article: An Adaptive Image Enhancement using Wiener Filtering with Compression and Segmentation},
	journal = {IJCA Proceedings on National Conference on Research Issues in Image Analysis and Mining Intelligence},
	year = {2015},
	volume = {NCRIIAMI 2015},
	number = {1},
	pages = {15-19},
	month = {June},
	note = {Full text available}
}

Abstract

Today information technology plays an eminent role in every fields of human survival. Due to the rapid development in the information processing system, and the huge data base become a challenging tasks. Due to the various issues in the text processing, image processing has been emerged to provide a solution to such issues using various stages viz. , image acquisition, image enhancement and image retrieval. In this paper a method for preprocessing of images and compression of filtered images with lossy and lossless compression is and segmentation is presented. Finally, this paper shows a compact image processing and the result of compared to evaluate the performance of the methods.

References

  • Chan, Raymond H. , Chung-Wa Ho, and Mila Nikolova. "Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. " Image Processing, IEEE Transactions on 14. 10 (2005): 1479-1485.
  • Davenport, Wilbur B. , and William L. Root. Random signals and noise. New York: McGraw-Hill, 1958.
  • Mythili, C. , and V. Kavitha. "Efficient Technique for Color Image Noise Reduction. " The research bulletin of Jordan, ACM 1. 11 (2011): 41-44.
  • Zhou, Huiyu, Jiahua Wu, and Jianguo Zhang. Digital Image Processing: Part II. Bookboon, 2010.
  • Abdallah, Yousif Mohamed Y. , and Abdalrahman Hassan. "Segmentation of Brain in MRI Images Using Watershed-based Technique. "
  • Rebelo, Ana, and Jaime S. Cardoso. "Staffline Detection in Grayscale Domain. "
  • Jiang, Yuan, and Zhi-Hua Zhou. "SOM ensemble-based image segmentation. " Neural Processing Letters 20. 3 (2004): 171-178.
  • Lakshmi, S. , and Dr V. Sankaranarayanan. "A study of edge detection techniques for segmentation computing approaches. " Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications (2010): 35-41.
  • Maini, Raman, and Himanshu Aggarwal. "Study and comparison of various image edge detection techniques. " International journal of image processing (IJIP) 3. 1 (2009): 1-11.
  • Davis, Geoffrey M. "A wavelet-based analysis of fractal image compression. " Image Processing, IEEE Transactions on 7. 2 (1998): 141-154.
  • Vemuri, B. C. , et al. "Lossless image compression. "
  • Acharjya, Pinaki Pratim, and Dibyendu Ghoshal. "Watershed segmentation based on distance transform and edge detection techniques. " International Journal of Computer Applications 52. 13 (2012): 583-598.
  • Belaid, Lamia Jaafar, and Walid Mourou. "Image segmentation: a watershed transformation algorithm. " Image Analysis & Stereology 28. 2 (2011): 93-102.
  • Tripatjot Singh,Sanjeev Chopra,Harmanpreet Kaur, Amandeep Kaur. "Image Compression Using Wavelet and Wavelet Packet Transformation. " IJCST Vol. 1, Issue 1, September 2010.
  • Albertus Joko Santoso, Dr. Lukito Edi Nugroho, Dr. Gede Bayu Suparta, Dr. Risanuri Hidayat. "Compression Ratio and Peak Signal to Noise Ratio in Grayscale Image Compression using Wavelet. " IJCST Vol. 2, Issue 2, June 2011.