An Efficient Image Denoising Based on Weiner Filter and NeighSure Shrink
NPriya B S1, Basavaraj N Jagadale2, Mukund N Naragund3, Vijayalaxmi Hegde4, Panchaxri5
1Priya B S*, Department of Electronics, Kuvempu University, Shimoga, India.
2Basavaraj N Jagadale, Department of PG Studies and Research in Electronics, Kuvempu University, Shimoga, India.
3Mukund N Naragund, Department of Physics and Electronics, CHRIST(Deemed to be University, Bengaluru, India.
4Vijayalaxmi Hegde, Department of Electronics, MESMM Arts and Science College, Sirsi, India.
5Panchaxri, Department of Electronics, SSA Govt. First grade College, Ballari, India.
Manuscript received on November 16, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 76-80 | Volume-9 Issue-2, December 2019. | Retrieval Number: A4905119119/2019©BEIESP | DOI: 10.35940/ijitee.A4905.129219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Weiner filter denoise the image using linear stochastic framework. It eliminates the noise by estimating optimal filter for noisy input image by minimizing the mean square error between the desired image and estimated image. The main drawback of this filter is the performance is reduced when the noise is random and unknown as it has fixed frequency response for all frequencies. The efficiency of this filter can be increased by incorporating method noise thresholding using NeighSure shrink. This paper presents a method which is a blend of Weiner filter and wavelet based NeighSure shrink thresholding. The results indicates that the proposed method is significantly superior than wavelet thresholding, Weiner filter and Gaussian filter with its method noise thresholding techniques in terms of visual quality, Peak Signal to noise ratio and image quality index.
Keywords: Discrete Wavelet Transform, Neigh Sure Shrink, Wavelet Thresholding, Weiner filter
Scope of the Article: Image analysis and Processing