Wavelet Based Video Denoising using Probabilistic Models

  • Azka Maqsood Military College of Signals, National University of Science and Technology, Rawalpindi, Pakistan
  • Imran Touqir Military College of Signals, National University of Science and Technology, Rawalpindi, Pakistan
  • Adil Masood Siddiqui Military College of Signals, National University of Science and Technology, Rawalpindi, Pakistan.
  • Maham Haider Military College of Signals, National University of Science and Technology, Rawalpindi, Pakistan

Abstract

Wavelet based image processing techniques do not strictly follow the conventional probabilistic models that are unrealistic for real world images. However, the key features of joint probability distributions of wavelet coefficients are well captured by HMT (Hidden Markov Tree) model. This paper presents the HMT model based technique consisting of Wavelet based Multiresolution analysis to enhance the results in image processing applications such as compression, classification and denoising. The proposed technique is applied to colored video sequences by implementing the algorithm on each video frame independently. A 2D (Two Dimensional) DWT (Discrete Wavelet Transform) is used which is implemented on popular HMT model used in the framework of Expectation-Maximization algorithm. The proposed technique can properly exploit the temporal dependencies of wavelet coefficients and their non-Gaussian performance as opposed to existing wavelet based denoising techniques which consider the wavelet coefficients to be jointly Gaussian or independent. Denoised frames are obtained by processing the wavelet coefficients inversely. Comparison of proposed method with the existing techniques based on CPSNR (Coloured Peak Signal to Noise Ratio), PCC (Pearson’s Correlation Coefficient) and MSSIM (Mean Structural Similarity Index) has been carried out in detail.The proposed denoising method reveals improved results in terms of quantitative and qualitative analysis for both additive and multiplicative noise and retains nearly all the structural contents of a video frame

Published
Jan 1, 2019
How to Cite
MAQSOOD, Azka et al. Wavelet Based Video Denoising using Probabilistic Models. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 38, n. 1, p. 17-30, jan. 2019. ISSN 2413-7219. Available at: <http://publications.muet.edu.pk/index.php/muetrj/article/view/729>. Date accessed: 23 mar. 2019. doi: http://dx.doi.org/10.22581/muet1982.1901.02.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License