Robust enhancement technique for color images corrupted by impulsive noise

In the paper, a novel approach to the enhancement of color images corrupted by impulsive noise is presented. The proposed algorithm first calculates for every image pixel the distances in the RGB color space to all elements belonging to the filtering window. Then, a sum of a specified number of smallest distances, which serves as a measure of pixel similarity, is calculated. This generalization of the Rank-Ordered Absolute Difference (ROAD) is robust to outliers, as the high distances are not considered when calculating this measure. Next, for each pixel, a neighbor with smallest ROAD value is searched for. If such a pixel is found, then the filtering window is moved to a new position and again a neighbor, with ROAD measure lower than the initial value is looked for. If it is encountered, the window is moved again, otherwise the process is terminated and the starting pixel is replaced with the last pixel in the path formed by the iterative procedure of the window shifting. The comparison with the filters intended for the removal of noise in color images revealed excellent properties of the new enhancement technique. It is very fast, as the ROAD values can be pre-computed, and the formation of the paths needs only comparisons of scalar values. The proposed technique can be applied for the restoration of color images distorted by impulsive noise and can also be used as a method of edge sharpening. Its low computational complexity allows also for its application in the processing of video sequences.

[1]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Ezequiel López-Rubio,et al.  Restoration of images corrupted by Gaussian and uniform impulsive noise , 2010, Pattern Recognit..

[3]  Weiren Shi,et al.  An Efficient Universal Noise Removal Algorithm Combining Spatial Gradient and Impulse Statistic , 2013 .

[4]  Amit Singer,et al.  Non-Local Euclidean Medians , 2012, IEEE Signal Processing Letters.

[5]  Takeo Kanade,et al.  Mode-seeking by Medoidshifts , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[6]  Bogdan Smolka.  On the robustified median filter for the reduction of impulsive noise in digital images , 2014 .

[7]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[8]  Stefano Soatto,et al.  Quick Shift and Kernel Methods for Mode Seeking , 2008, ECCV.

[9]  Charles K. Chui,et al.  A universal noise removal algorithm with an impulse detector , 2005, IEEE Transactions on Image Processing.

[10]  Rastislav Lukac,et al.  Sharpening vector median filters , 2007, Signal Process..

[11]  Bogdan Smolka,et al.  Fast adaptive switching technique of impulsive noise removal in color images , 2016, Journal of Real-Time Image Processing.

[12]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[14]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

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

[16]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Sung-Jea Ko,et al.  New autofocusing technique using the frequency selective weighted median filter for video cameras , 1999, 1999 Digest of Technical Papers. International Conference on Consumer Electronics (Cat. No.99CH36277).

[18]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[19]  Guillermo Peris-Fajarnés,et al.  Rank-Ordered Differences Statistic Based Switching Vector Filter , 2006, ICIAR.