Fuzzy Logic Recursive Gaussian Denoising of Color Video Sequences via Shot Change Detection
- 1 Department of Computer Science and Engineering, University College of Engineering Ramanathapuram, Pullangudi, Tamil Nadu, India
- 2 Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
- 3 Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
Abstract
A new technique using fuzzy in a recursive fashion is presented to deal with the Gaussian noise. In this technique, the keyframes and between frames are identified initially and the keyframe is denoised efficiently. This frame is compared with the between frames to remove noise. To do so the frames are partitioned into blocks; the motion vector is calculated; also the difference is measured using the dissimilarity function. If the blocks have no motion vectors in the block, the block of value is copied to the between frames otherwise the difference between the blocks is calculated and this value is filtered with temporal filtering. The blocks are processed in overlapping manner to avoid the blocking effect and also to reduce the additional edges created while processing. The simulation results show that the peak signal to noise ratio of the new technique is improved up to 1 dB and also the execution time is greatly reduced.
- Tomasi, C. and Manduchi, R. (1998) Bilateral Filtering for Gray and Color Images: Proceedings of the 6th International Conference on Computer Vision, 4-7 January 1998, 839-846. http://dx.doi.org/10.1109/iccv.1998.710815
- Elad, M. (2002) On the Origin of the Bilateral Filter and Ways to Improve It. IEEE Transactions on Image Processing, 11, 1141-1151. http://dx.doi.org/10.1109/TIP.2002.801126
- Rajagopalan, R. and Orchard, M. (2002) Synthesizing Processed Video by Filtering Temporal Relationships. IEEE Transactions on Image Processing, 11, 26-36. http://dx.doi.org/10.1109/83.977880
- Balster, E.J. and Ewing, R.L. (2006) Combined Spatial and Temporal Domain Wavelet Shrinkage Algorithm for Video Denoising. IEEE Transactions on Circuits and Systems for Video Technology, 16, 220-230. http://dx.doi.org/10.1109/TCSVT.2005.857816
- Lee, S.-W. Maik, V., Jang, J.-H., Shin, J. and Paik, J. (2005) Noise Adaptive Spatio-Temporal Filter for Real-Time Noise Removal in Low Light Level Images. IEEE Transactions on Consumer Electronics, 51, 648-653. http://dx.doi.org/10.1109/TCE.2005.1468014
- Ercole, C., Foi, A., Katkovnik, V. and Egiazarian, K. (2005) Spatio-Temporal Pointwise Adaptive Denoising of Video: 3d Non-Parametric Regression Approach. First Workshop on Video Processing and Quality Metrics for Consumer Electronics, January 2005.
- Varghese, G. and Wang, Z. (2010) Video Denoising Based on a Spatio-Temporal Gaussian Scale Mixture Model. IEEE Transactions on Circuits and Systems for Video Technology, 20, 1032-1040. http://dx.doi.org/10.1109/TCSVT.2010.2051366
- Rajpoot, N., Yao, Z. and Wilson, R. (2004) Adaptive Wavelet Restoration of Noisy Video Sequences. Proceedings of the IEEE International Conference on Image Processing, Singapore, 24-27 October 2004, 957-960. http://dx.doi.org/10.1109/icip.2004.1419459
- Amer, A. and Schrerder, H. (1996) A New Video Noise Reduction Algorithm Using Spatial Subbands. Proceedings of the International Conference on Electronic Circuits and Systems, Rhodos, 13-16 October 1996, 45-48. http://dx.doi.org/10.1109/ICECS.1996.582658
- Selesnick, W. and Li, K. (2003) Video Denoising Using 2d and 3d Dual-Tree Complex Wavelet Transforms. Proceedings of the SPIE Wavelet Applications in Signal and Image Processing, 14 November 2003, 607-618. http://dx.doi.org/10.1117/12.504896
- Zhang, D., Han, J.-W.O., Kwon, J., Nam, H.-M. and Ko, S.-J. (2011) A Saliency Based Noise Reduction Method for Digital TV. Proceedings of the IEEE International Conference on Consumer Electronics (ICCE’11), Las Vegas, 9-12 January 2011, 743-744. http://dx.doi.org/10.1109/icce.2011.5722840