Noise estimation and filtering using block-based singular value decomposition

Preprocessing of image and video sequences with spatial filtering techniques usually improves the image quality and compressibility. We present a block-based, nonlinear filtering algorithm based on singular value decomposition and compression-based filtering. Experiments show that the proposed filter preserves edge details and can significantly improve the compression performance.

[1]  Balas K. Natarajan Filtering random noise from deterministic signals via data compression , 1995, IEEE Trans. Signal Process..

[2]  Vasudev Bhaskaran,et al.  Effective nearly lossless compression of digital video sequences via motion-compensated filtering , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Konstantinos Konstantinides,et al.  Occam filters for stochastic sources with application to digital images , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[4]  Konstantinos Konstantinides,et al.  Statistical analysis of effective singular values in matrix rank determination , 1988, IEEE Trans. Acoust. Speech Signal Process..

[5]  H. Andrews,et al.  Singular value decompositions and digital image processing , 1976 .

[6]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[7]  A. Murat Tekalp,et al.  Efficient multiframe Wiener restoration of blurred and noisy image sequences , 1992, IEEE Trans. Image Process..