The week's pick
Random Articles
Reseach Article
An Effective Technique of Image Degradation using DWT based Padding Kernel Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 170 - Number 3 |
| Year of Publication: 2017 |
| Authors: Varsha Sharma, Ajay Goyal |
10.5120/ijca2017914740
|
Varsha Sharma, Ajay Goyal . An Effective Technique of Image Degradation using DWT based Padding Kernel Detection. International Journal of Computer Applications. 170, 3 ( Jul 2017), 28-33. DOI=10.5120/ijca2017914740
Abstract
Image Filtering is a technique of removing unwanted Noise from image so that the image can be improved in terms of brightness and noise and contrast. Although there are various technique implemented for the Image Degradation and removing Noise level from image such as using Gaussian Blur. The Existing Gaussian Blur technique is an efficient technique which provides more Peak Signal to Noise Ratio and Less Error rate as compare to Zhang Distance and Local Phase Quantization Algorithms. But the technique implemented is not feasible in terms of all images and PSNR and Error Rate, Hence a new and efficient technique is proposed in the paper which is based on the concept of kernel and padding. This work also applies the Haar wavelet Transform for filtering the image in order to reconstruct image which have the noise and blur. The results of this paper show that the proposed method gives the better result from the previous methods. It seems to be that the PSNR, Mean Square Rate and execution time is better in the proposed scheme.
References
- S.Ramya, T.Mercy Christial “Restoration of blurred images using Blind Deconvolution Algorithm”, 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp. 496 – 499, March 2011.
- Jinlian Zhuang and Youshen Xia “A Two-Dimensional Iterative Algorithm for Blind Image Restoration based on An L1 Regularization Approach”, 2010 3rd International Congress on Image and Signal Processing (CISP2010), pp. 51 – 55, 2010.
- E. Ordentlich, M. Weinberger, G. Seroussi,"A low-complexity modeling approach for embedded coding of wavelet coefficients", In Proc. IEEE Data Compression Conf., Snowbird, UT, pp. 408-417, Mar. 1998.
- W.A.Pearlman,"Performance bounds for sub-band codes", Chapter 1 in Sub-band Image Coding, J. W. Woods and Ed. Klvwer. Academic Publishers, 1991.
- A.Said, W.A. Pearlman,"A new, fast and efficient image codec based on set partitioning in hierarchical trees", IEEE Trans. on Circuits and Systems for Video Technology 6, pp. 243-250, June 1996.
- P.Schelkens, "Multi-dimensional wavelet coding algorithms and implementations", Ph.D dissertation, Department of Electronics and Information Processing, Vrije Universitie Brussel, Brussels, 2001.
- 'Digital Image Restoration', by M.R. Banham and A.K. Katsaggelos, IEEE Signal Processing Magazine, pp. 27-41, March 1997.
- S.Ramya, T.Mercy Christial “Restoration of blurred images using Blind Deconvolution Algorithm”, International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp. 496 – 499, 2011.
- Ryu Nagayasu, Naoto Hosoda, Nari Tanabe, Hideaki Matsue, Toshihiro Furukawa “Restoration Method For Degraded Images Using Two-Dimensional Block Kalman Filter With Colored Driving Source”, IEEE Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE), pp. 151 – 156, 2011.
- Sun qi, Hongzhi Wang , Lu wei, “ An iterative blind deconvolution image restoration algorithm based on adaptive Selection of regularization parameter”, Third International Symposium on Intelligent Information Technology Application (IITA 2009), pp. 112 – 115, 2009.
- Jong-Ho Lee, Yo-Sung H “High-quality non-blind image deconvolution with adaptive regularization”, Journal of Visual Communication and Image Representation, Volume 22, Issue 7, pp. 653-663, 2011.
- Anna Tonazzini, Ivan Gerace, and Francesca Martinelli “Multichannel Blind Separation and Deconvolution of Images for Document Analysis”, IEEE Transactions on Image Processing, Vol. 19, No. 4, pp. 912-925, April 2010.
- Mariana S. C. Almeida and Luís B. Almeida “Blind and Semi-Blind Deblurring of Natural Images”, IEEE Transactions on Image Processing, Vol. 19, No. 1, pp. 36-52, January 2010.
Index Terms
Keywords