Semi Blind Restoration of Diversified Field Images
Pratik D. Shah1, Anil L. Wanare2
1Pratik D. Shah, Lecturer, Department of Electronics and Telecommunication, Dr. D.Y. Patil School of Engineering, University of Pune (M.H), India.
2Anil L. Wanare, Assistant Professor, Department of Electronics and Telecommunication, G.H. Raisoni Institute of Engineering and Technology, University of Pune (M.H), India.
Manuscript received on 21 July 2013 | Revised Manuscript received on 28 July 2013 | Manuscript published on 30 July 2013 | PP: 66-70 | Volume-2 Issue-3, July 2013 | Retrieval Number: C0711072313/2013©BEIESP
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: This paper is concerned with critical performance analysis of spatial linear restoration techniques for still images from various fields (Medical, Natural and Arial images).The performances of the linear restoration techniques are provided with possible combination of various additive noises and images from diversified fields. Efficiency of linear restoration techniques according to difference distortion and correlation distortion metrics is computed. Tests performed on monochrome images, with various synthetic and real-life degradations, without and with noise, in single frame scenarios, showed good results, both in subjective terms and in terms of the increase of signal to noise ratio (ISNR) measure. The comparison of the present approach with previous individual methods in terms of mean square error, peak signal-to-noise ratio, and normalized absolute error is also provided. In comparisons with other state of art methods, our approach yields better to optimization, and shows to be applicable to a much wider range of noises. We discuss how experimental results are useful to guide to select the effective combination.
Keywords: About Additive Noise, Correlation Distortion Metrics, linear Image Restoration, Monochrome Image Denoising, Wiener Filter.
Scope of the Article: Signal and Image Processing