Modified Double Bilateral Filter for Sharpness Enhancement and Noise Removal

This paper propose a double bilateral filter that extends the classical bilateral filtering for image noise removal. A new median-metric weighting function is introduced by incorporating a 3 × 3 median filter into a second bilateral filter. A variety of images contaminated by various degrees of Gaussian, impulse, and mixed noise were used to assess the performance of this new filtering method. It is indicated that the double bilateral filter outperformed several existing methods in both visual image quality and restored signal quantity. In this paper, we propose a Modified double bilateral filter where we replaced 3 × 3median filter with a new decision-based algorithm based median filter in the second bilateral filter. The proposed method, unlike other nonlinear filters, removes only corrupted pixel by the median value or by its neighboring pixel value. As a result of this, the proposed method removes the noise effectively even at noise level as high as 90% and preserves the edges without any loss up to 80% of noise level. The new proposed median filter is for restoration of images that are highly corrupted by impulse noise.

[1]  H. Hu,et al.  Classification-based hybrid filters for image processing , 2006, Electronic Imaging.

[2]  Charles A. Bouman,et al.  AM/FM halftoning: digital halftoning through simultaneous modulation of dot size and dot density , 2004, J. Electronic Imaging.

[3]  Alexei A. Efros,et al.  Fast bilateral filtering for the display of high-dynamic-range images , 2002 .

[4]  E. Dubois,et al.  Digital picture processing , 1985, Proceedings of the IEEE.

[5]  W. Clem Karl,et al.  3.6 – Regularization in Image Restoration and Reconstruction , 2005 .

[6]  Jan P. Allebach,et al.  Optimal image scaling using pixel classification , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[7]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).