Implementation and Analysis of Image Restoration Techniques
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© May to June Issue 2011 by IJCTT Journal|
|Volume-1 Issue-2 |
|Year of Publication : 2011|
|Authors : Charu Khare, Kapil Kumar Nagwanshi.|
Charu Khare, Kapil Kumar Nagwanshi. "Implementation and Analysis of Image Restoration Techniques"International Journal of Computer Trends and Technology (IJCTT),V1(2):195-200 May to June Issue 2011 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract—IMAGE restora tion is an important issue in high - level image processing. Images are often degraded during the data acquisition process. The degradation may involve blurring, information loss due to sampling, quantization effects, and various sources of noise. The purpos e of image restoration is to estimate the original image from the degraded data. It is widely used in various fields of applications, such as medical imaging, astronomical imaging, remote sensing, microscopy imaging, photography deblurring, and forensic sc ience, etc. Often the benefits of improving image quality to the maximum possible extent for outweigh the cost and complexity of the restoration algorithms involved. In this paper we are comparing various image restoration techniques like Rich ardson - Lucy a lgorithm, Wie ner filter, Neural Network approach, on the basis of PSNR (Peak Signal to Noise Ratio).
 Aizenberg I., Bregin T., Butakoff C., Karnaukhov V., Merzlyakov N. and Milukova O., "Type of Blur and Blur Parameters Identification Using Neural Network and Its Application to Image Restoration". In: J.R. Dorronsoro (ed.) Lecture Notes in Computer S cience, Vol. 2415, Springer - Verlag, Berlin, Heidelberg , New York (2002) 1231 - 1236.
 Aizenberg I., Myasnikova E., Samsonova M. and Reinitz J., “Temporal Classification of Drosophila Segmentation Gene Expression Patterns by the Multi - Valued Neural Recognition Method”, Journal of Mathematical Biosciences, Vol.176 (1) (2002) 145 - 159.
 Aizenberg I., Paliy D. and Astola, J.T. “Multilayer Neural Network based on Multi - Valued Neurons and the Blur Identification Problem”, accepted to the IEEE World Congress on Computa tional Intelligence,Vancouver, to appear: July, 2006 Katkovnik V., Egiazarian K. and Astola J., "A spatially adaptive nonparametric image deblurring", IEEE Transactions on Image Processing, Vol. 14, No. 10 (2005) 1469 - 1478.
 Ali Said Ali Awad, "A Comparisi on Between Previously known and Two Novel Image Restoration Algorithm" .
 Aoki H., Watanabe E., Nagata A. and Kosugi Y. "Rotation - Invariant Image Association for Endoscopic Positional Identification Using Complex - Valued Associative Memories", In: J. Mira, A .
 C. Helstrom, “Image Restoration by the Method of Least Squares”, J. Opt . Soc.Amer., 57(3): 297 - 303, March 1967.
 D. Kundur and D. Hatzinakos, “A novel blind deconvolution scheme for image restoration using recursive filtering,” IEEE Trans. Signal Proces s., vol. 46, n o. 2, pp. 375 - 390, Feb. 1998.
 Erhan A.Ince, Ali S. Awad, “Karesel Hata Ölçütü Ve Seçilmis Bir Esik Derine Bagli Tek - Boyutlu Netlestirme Yöntemi”, SIU 2001, Turkey, no.9, vol.1, pp.366 - 369, 25 April, 2001.
 G. Pavlovi ´c and A. M. Tekalp, “Maximum likelihood parametric blur identification based on a continuous spatial domain model,” IEEE Trans. Image Process., vol. 1, no. 10, pp. 496 - 504, Oct. 1992.
 H. C. Andrews and B. R. Hunt, “ Digital Image Restoration” , Prentice Hall , Englewood Cliff NJ , 1977.
 K. R. Castleman, “ Digital Image Processing” , International Edition, Prentice - Hall, Inc., 1996.
 M. M. Chang, A. M. Tekalp, and A. T. Erdem, “Blur identification using the bispectrum,” IEEE Trans. Acoust., Speech, Signal Process., vol. 39, no. 5, pp . 2323 - 2325, Oct. 1991.
 Muezzinoglu M. K., Guzelis C. and Zurada J. M., "A New Design Method for the Complex - Valued Multistate Hopfield Associative Memory", IEEE Trans. on Neural Networks, V ol. 14, No 4 (2003) 891 - 899.
 Neelamani R., Choi H., and Baraniuk R. G., "Forward: Fourier - wavelet regularized deconvolution for ill - conditioned systems", IEEE Trans. on Signal Processing, V ol. 52, No 2 (2003) 418 - 433.
 Prieto (eds.) Bio - inspired Applications of Connectionism. Lecture Notes in Computer Science, Vol. 2085 Springer - Verlag, Berlin Heidelbe rg New York (2001) 369 - 374.
 R. L. Lagendijk, J. Biemond, and D. E. Boekee, “Blur identification using the expectation - maximization algorithm,” in Proc. IEEE. Int. Conf. Acoustics, Speech, Signal Process., vol. 3 7, Dec. 1989 , pp. 1397 - 1400.
Keywords— Peak Signal to noise ratio, Image Restoration, Degradation model, Neural Network approach , Richardson - Lucy algorithm, Wie ner Filter.