Novel Video Denoising Using 3-D Transformation Techniques
Matheel E. Abdulmunim1, Rabab F. Abass2
1Dr. Matheel E. Abdulmunim, Assist. Prof. Computer Sciences Dep, Head of Multimedia part. University of Technology, Baghdad, Iraq.
2Rabab.F. Abass, Computer Sciences Dep. Master Student ,University of Technology, Baghdad, Iraq.
Manuscript received on May 19, 2013. | Revised Manuscript received on June 15, 2013. | Manuscript published on June 30, 2013. | PP: 318-324 | Volume-2, Issue-5, June 2013. | Retrieval Number: E1823062513/2013©BEIESP
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© 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: Digital videos are often corrupted by a noise during the acquisition process, storage and transmission. It made the video in ugly appearance and also affect on another digital video processes like compression, feature extraction and pattern recognition so video denoising is highly desirable process in order to improve the video quality. There are many transformation for denoising process, one of them are Fast Discrete Wavelet Transform(FDWT) and framelet transform (Double-Density Wavelet Transform) which is a perfect in denoising process by avoiding the problems in the other transformations. In this paper we propose a method named Translation Invariant with Wiener filter (TIW) this method is proposed to solve the shift variance problem and use this method to denoise a noisy video with Gaussian white noise type.. It is applied with Two Dimensional Fast Discrete Wavelet Transform(2-D FDWT), Three Dimensional Fast Discrete Wavelet Transform(3-D FDWT), Two Dimensional Double Density Wavelet Transform(2-D DDWT) and Three Dimensional Double Density Wavelet Transform(3-D DDWT). The results show that our (TIW) gives a better denoising results comparative with the original methods.
Keywords: Fast Discrete Wavelet Transform, Three Dimensional Fast Discrete Wavelet Transform, Double-Density Wavelet Transform, hard threshod, soft threshold, semisoft threshold, Translation Invariant Wiener filter (TIW).