A novel image detail preserving impulse noise removal algorithm

In this paper, we introduce a novel edge directed image detail preserving impulse noise removal algorithm. Our approach is based on local image directionality features. Local edge features are analysed on both a downsampled version of the image and the noisy image, and both soft an sharp edges are considered for selective noise removal. Experimental results on a set of standard images show our technique to be effective in removing salt-and-pepper noise even at high noise levels, to yield good image quality and to outperform a number of other noise removal techniques.

[1]  Mila Nikolova,et al.  Regularizing Flows for Constrained Matrix-Valued Images , 2004, Journal of Mathematical Imaging and Vision.

[2]  V. Radha,et al.  Enhanced Switching Median Filter for Denoising in 2D Patterned Textile Images , 2012 .

[3]  T. Nodes,et al.  Median filters: Some modifications and their properties , 1982 .

[4]  Raymond H. Chan,et al.  Minimization of a Detail-Preserving Regularization Functional for Impulse Noise Removal , 2007, Journal of Mathematical Imaging and Vision.

[5]  Guy Gilboa,et al.  Nonlocal Operators with Applications to Image Processing , 2008, Multiscale Model. Simul..

[6]  David Zhang,et al.  Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  David Ebenezer,et al.  A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises , 2007, IEEE Signal Processing Letters.

[8]  G. Sivaradje,et al.  Switching Median Filter for Image Enhancement , 2012 .

[9]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[10]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Wenbin Luo.  Efficient removal of impulse noise from digital images , 2006, IEEE Transactions on Consumer Electronics.

[12]  Jian-Feng Cai,et al.  Two-phase approach for deblurring images corrupted by impulse plus gaussian noise , 2008 .

[13]  Richard A. Haddad,et al.  Adaptive median filters: new algorithms and results , 1995, IEEE Trans. Image Process..

[14]  J.A.R. Blais,et al.  Adaptive Median Filtering Using Local Entropy , 1990, 10th Annual International Symposium on Geoscience and Remote Sensing.