ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Detail Preserved Denoising Algorithm Via Patch Based Edge Similarity Index And Joint Bilateral Filter


Affiliations
1 Department of Department of Agricultural Statistics, Kerala Agricultural University, India
     

   Subscribe/Renew Journal


Denoising algorithms are getting more attention in these days because they play a vital role in other applications of image processing. As details structures are the important information of Human Visual System, its preservation in denoised image is highly demanding one. A two-step denoising algorithm based on Patch based Edge Similarity Index and Joint Bilateral Filter algorithm proposed in this paper, preserves the edge structures and produce visually pleasant denoised image. Edge Similarity Index (ESI) proposed in the paper group patches according to their similarity in orientation and hence preserve the detail feature. The optimally grouped patches transferred to Principal Component Analysis (PCA) domain and the proposed noise suppression method eliminates the noisy component. Adaptive soft thresholding noise suppression method suppress the noise based on local noise estimation. Noise estimation in local level helps to estimate the noise accurately where noise affected differently in regions of a scene. Strong noises residuals may exist after first step, the denoised image in the first step further processed by a Joint Bilateral Filter for producing visually pleasant denoised image. Experimental results shown that proposed denoising algorithm achieves comparable detail preserving performance in terms of visual analysis and quantitative analysis over other state of art method.

Keywords

Fixed Patch, Variable Patch, Denoising Patch, Edge Similarity Index, Joint Bilateral Filter
Subscription Login to verify subscription
User
Notifications
Font Size

  • C. Tomasi and R. Manduchi, “Bilateral Filtering for Gray and Color Images”, Proceedings of IEEE International Conference on Computer Vision, pp. 839-846, 1998.

  • P. Perona and J. Malik, “Scale-Space and Edge Detection using Anisotropic Diffusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, pp. 629-639, 1990.

  • H. Takeda, S. Farsiu and P. Milanfar, “Kernel regression for Image Processing and Reconstruction”, IEEE Transactions on Image Processing, Vol. 16, No. 2, pp. 349-366, 2007.

  • A. Buades, B. Coll and J. Morel, “A Non-Local Algorithm for Image Denoising”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1-12, 2005.

  • V. Katkovnik, A. Foi, K. Egiazarian and J. Astola, “From Local Kernel to Nonlocal Multiple-Model Image Denoising”, International Journal on Computer Vision, Vol. 86, No. 1, pp. 1-32, 2010.

  • K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, “Image Denoising by Sparse 3D Transform-Domain Collaborative Filtering”, IEEE Transactions on Image Processing, Vol. 16, No. 8, pp. 2080-2095, 2007.

  • L.Z. Manor, K. Rosenblum and Y.C. Eldar, “Dictionary Optimization for Block-Sparse Representations”, IEEE Transactions on Signal Processing, Vol. 60, No.5, pp. 23862395, 2012.

  • P. Chatterjee and P. Milanfar, “Patch-Based Near-Optimal Image Denoising”, IEEE Transactions on Image Processing, Vol. 21, No. 4, pp. 1635-1649, 2012.

  • Y. Ding and I.W. Selesnick, “Artifact-Free Wavelet Denoising: Non-Convex Sparse Regularization, Convex Optimization”, IEEE Transactions on Signal Processing Letters, Vol. 22, No. 9, pp. 1364-1368, 2015.

  • A.E. Cetin and M. Tofighi, “Projection-Based Wavelet Denoising”, IEEE Signal Processing Magazine, Vol. 32, No. 5, pp. 120-124, 2015.

  • Sergey Krivenko, Vladimir Lukin, Benoit Vozel and Kacem Chehdi, “Prediction of DCT-Based Denoising Efficiency for Images Corrupted by Signal-Dependent Noise”, Proceedings of IEEE 34th International Scientific Conference on Electronics and Nanotechnology, pp. 1-12, 2014.

  • K. Fukunaga, “Introduction to Statistical Pattern Recognition”, 2nd Edition, Academic Press, 1991.

  • L. Zhang,W. Dong, D. Zhang and G. Shi, “Two-Stage Image Denoising by Principal Component Analysis with Local Pixel Grouping”, Pattern Recognition, Vol. 43, No. 4, pp. 1531-1549, 2010.

  • Wenzhao Zhao, Yisong Lv, Qiegen Liu and Binjie Qin, “Detail-Preserving Image Denoising Via Adaptive Clustering and Progressive PCA Thresholding”, IEEE Access, Vol. 6, pp. 6303-6315, 2018.

  • D.D. Muresan and T.W. Parks, “Adaptive Principal Components and Image Denoising”, Proceedings of International Conference on Image Processing, Vol. 1, pp. 101-104, 2003.

  • Hancheng Yu, Li Zhao and Haixian Wang, “Image Denoising using Trivariate Shrinkage Filter in the Wavelet Domain and Joint Bilateral Filter in the Spatial Domain”, IEEE Transactions on Image Processing, Vol. 18, No. 10, pp. 2364-2369, 2009.

  • C. Tomasi and R. Manduchi, “Bilateral Filtering for Gray and Color Images”, Proceedings of International Conference on Computer Vision, pp. 839-846, 1998.

  • T. Randen. “Brodatz Textures”, Available at: http://www.ux.uis.no/~tranden/brodatz.html, Accessed at 2007.

  • Mansour Nejati, Shadrokh Samavi, Harm Derksen and Kayvan Najarian, “Denoising by Low-Rank and Sparse Representations”, Journal of Visual Communication and Image Representation, Vol. 36, pp. 28-39, 2016.

  • Shuhang Gu, Lei Zhang, Wangmeng Zuo and Xiangchu Feng, “Weighted Nuclear Norm Minimization with Application to Image Denoising”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862-2869, 2014.

  • Y. Wu, L. Fang and S. Li. “Weighted Tensor Rank-1 Decomposition for Nonlocal Image Denoising”, IEEE Transactions on Image Processing, Vol. 28 No. 6, pp. 2719-2730, 2019.

  • F. Sattar, L. Floreby, G. Salomonsson and B. “Lovstrom. Image Enhancement based on a Nonlinear Multiscale Method”, IEEE Transactions on Image Processing, Vol. 6, No. 6, pp. 888-895, 1997.


Abstract Views: 235

PDF Views: 0




  • Detail Preserved Denoising Algorithm Via Patch Based Edge Similarity Index And Joint Bilateral Filter

Abstract Views: 235  |  PDF Views: 0

Authors

Dayana David
Department of Department of Agricultural Statistics, Kerala Agricultural University, India

Abstract


Denoising algorithms are getting more attention in these days because they play a vital role in other applications of image processing. As details structures are the important information of Human Visual System, its preservation in denoised image is highly demanding one. A two-step denoising algorithm based on Patch based Edge Similarity Index and Joint Bilateral Filter algorithm proposed in this paper, preserves the edge structures and produce visually pleasant denoised image. Edge Similarity Index (ESI) proposed in the paper group patches according to their similarity in orientation and hence preserve the detail feature. The optimally grouped patches transferred to Principal Component Analysis (PCA) domain and the proposed noise suppression method eliminates the noisy component. Adaptive soft thresholding noise suppression method suppress the noise based on local noise estimation. Noise estimation in local level helps to estimate the noise accurately where noise affected differently in regions of a scene. Strong noises residuals may exist after first step, the denoised image in the first step further processed by a Joint Bilateral Filter for producing visually pleasant denoised image. Experimental results shown that proposed denoising algorithm achieves comparable detail preserving performance in terms of visual analysis and quantitative analysis over other state of art method.

Keywords


Fixed Patch, Variable Patch, Denoising Patch, Edge Similarity Index, Joint Bilateral Filter

References