A Unit Plane Edge on-off Slope Algorithm Based Fast LTVR Res-toration Analysis

  • Authors

    • K. Praveen Kumar
    • C. Venkata Narasimhulu
    • K. Satya Prasad
    2018-11-28
    https://doi.org/10.14419/ijet.v7i4.20.22116
  • Image denoising, filter, ON-OFF, edge, restoration

  • This research paper presents a Fast LTVR (Localized Total Variation Regularized) method for restoring the degraded images by white noise, while preserving the image edge details in a constructed unit plane edge model through a Unit Plane Edge ON-OFF Slope algorithm. The noisy image contains two details; one with high noise and the other with edge fined details. The edge fine details are restored using ON-OFF Slope algorithm. The denoised image and the edge fine details are used to reconstruct the final restored image. A Unit Plane Edge restoration method is proposed in this research work to estimate the edge-mapping with the fine details. Simulation results of proposed work shows an effective image restoration algorithm comparatively with different filter based restoration methods.  

     

     

  • References

    1. [1] E. Ercelebi and S. Koc, “Lifting-based wavelet domain adaptive Wiener filter for image enhancement †IEE Proc. Vis. Image and Signal Processing, vol.153, no.1, pp. 31–36, Feb. 2006.

      [2] S. Suhaila and T. Shimamura “Power spectrum estimation method for image denoising by frequency domain Wiener filter †in Proc. IEEE Int. Conf. Computer and Automation Engineering, Singapore, 2010, pp. 608–612.

      [3] S. Suhaila, and T. Shimamura, "Image Restoration Based on Edgemap and Wiener Filter for Preserving Fine Details and Edges", International Journal Of Circuits, Systems And Signal Processing, Issue 6, Volume 5,pp-618-626, 2011.

      [4] G. Gilboa, N. Sochen, and Y. Zeevi “Texture preserving variational denoising using an adaptive fidelity term †in Proc. IEEE Workshop Variational and Level Set Methods in Computer Vision, Nice, 2003, pp.137–144.

      [5] Buades, B. Coll and J M Morel “A non-local algorithm for image denoising †in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, San Diego, 2005, vol.2, pp. 60–65.

      [6] W. Dong, G. Shi, Y. Ma, and X. Li, “Image Restoration via Simultaneous Sparse Coding: Where Structured Sparsity Meets Gaussian Scale Mixture,†International Journal of Computer Vision (IJCV), vol. 114, no. 2, pp. 217-232, Sep. 2015.

      [7] Jian Zhang, Debin Zhao, Wen Gao, “Group-based Sparse Representation for Image Restoration,†IEEE Transactions on Image Processing, 15-July-2015.

      [8] Vardan Papyan and Michael Elad, Multi-Scale Patch-Based Image Restoration, IEEE Transactions on Image Processing, vol. 25, no. 1, pages 249-261, January 2016.

      [9] Uwe Schmidt and Stefan Roth, “Shrinkage Fields for Effective Image Restoration,†IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, June 2014.

      [10] Kheradmand and P. Milanfar, “A general framework for regularized, similarity-based image restoration,†IEEE Transactions on Image Processing, vol. 23, no. 12, pp. 5136-5151, Dec 2014.

      [11] C. H. Hsieh, P. C. Huang and S. Y. Hung, “Noisy image restoration based on boundary resetting BDND and median filtering with smallest window †WSEAS Trans. Signal Processing, vol.5, no.5, pp. 178–187, May 2009.

      [12] V. Gui and C Caleanu “On the effectiveness of multiscale mode filters in edge preserving †in Proc. WSEAS Int. Conf. Systems, Rodos Island, 2009, pp. 190–195.

      [13] S. Suhaila and T. Shimamura “Power spectrum estimation method for image denoising by frequency domain wiener filter †in Proc. IEEE Int. Conf. Computer and Automation Engineering, Singapore, 2010, pp. 608–612.

      [14] Zhang Y, Pu Y-F, Hu J-R and Zhou J-L 2012 A class of fractional order variational image inpainting models. Appl. Math. Inf. Sc. Int. J. 2: 299–306

      [15] M. V. Afonso, J. M. Bioucas-Dias, and M. A. T. Figueiredo, “An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems,†Image Processing, IEEE Transactions on, vol. 20, no. 3, pp. 681 – 695, Mar. 2011.

      [16] Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,†SIAM Journal of Imaging Science, vol. 2, no. 1, pp. 183–202, Mar 2009.

      [17] Y. Huang, M. Ng, and T. Zeng, “The convex relaxation method on deconvolution model with multiplicative noise,†Commun. Comput. Phys., vol. 13, no. 4, pp. 1066–1092, 2013.

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  • How to Cite

    Praveen Kumar, K., Venkata Narasimhulu, C., & Satya Prasad, K. (2018). A Unit Plane Edge on-off Slope Algorithm Based Fast LTVR Res-toration Analysis. International Journal of Engineering & Technology, 7(4.20), 27-30. https://doi.org/10.14419/ijet.v7i4.20.22116