Published In : IJCAT Journal Volume 2, Issue 2

Date of Publication : March 2015

Pages : 57 - 61

Figures :05

Tables : --

Publication Link :Spotless Image Quality Improvement Using Novel Patch Reordering Algorithm

 

 

 

Kiran Prajapati : SBITM College of Engineering Betul , India

Sachin Choudhari : SBITM College of Engineering Betul , India

 

 

 

 

 

 

 

Interpolation

Denoising

Filtering

Pixel- Reordering

Patch-Reordering

The implemented patch reordering algorithm for noisy image provide a new image quality enhancement approach for smooth patch reordering of the pixels in the given image. Our implemented approach uses operation such as interpolation and median filtering; the given framework achieves high quality results.

 

 

 

 

 

 

 

 

 

[1] A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model. Simul., vol. 4, no. 2, pp. 490–530, 2006. [2] P. Chatterjee and P. Milanfar, “Clustering-based denoising with locally learned dictionaries,” IEEE Trans. Image Process., vol. 18, no. 7, pp. 1438–1451, Jul. 2009. [3] G. Yu, G. Sapiro, and S. Mallat, “Image modeling and enhancement via structured sparse model selection,” in Proc. 17th IEEE Int. Conf. Image Process., Sep. 2010, pp. 1641–1644. [4] G. Yu, G. Sapiro, and S. Mallat, “Solving inverse problems with piecewise linear estimators: From Gaussian mixture models to structured sparsity,” IEEE Trans. Image Process., vol. 21, no. 5, pp. 2481–2499, May 2012. [5] W. Dong, X. Li, L. Zhang, and G. Shi, “Sparsitybased image denoising via dictionary learning and structural clustering,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2011, pp. 457–464. [6] W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and superresolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process., vol. 20, no. 7, pp. 1838–1857, Jul. 2011. [7] D. Zoran and Y. Weiss, “From learning models of natural image patches to whole image restoration,” in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 479–486. [8] M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736–3745, Dec. 2006. [9] J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process., vol. 17, no. 1, pp. 53–69, Jan. 2008. [10] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, “Non-local sparse models for image restoration,” in Proc. IEEE 12th Int. Conf. Comput. Vis., Sep.–Oct. 2009, pp. 2272–2279. [11] R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” in Proc. 7th Int. Conf. Curves Surf., 2012, pp. 711–730. [12] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080–2095, Aug. 2007. [13] X. Li, “Patch-based image interpolation: Algorithms and applications, Proc. Int. Workshop Local Non- Local Approx. Image Process., 2008, pp. 1–6. [14] I. Ram, M. Elad, and I. Cohen, “Generalized treebased wavelet transform,” IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4199–4209, Sep. 2011. [15] I. Ram, M. Elad, and I. Cohen, “Redundant wavelets on graphs and high dimensional data clouds,” IEEE Signal Processing Letters, vol. 19, no. 5, pp. 291–294, May 2012. [16] G. Plonka, “The easy path wavelet transform: A new adaptive wavelet transform for sparse representation of two-dimensional data,” Multiscale Model. Simul., vol. 7, no. 3, pp. 1474–1496, 2009. [17] D. Heinen and G. Plonka, “Wavelet shrinkage on paths for denoising of scattered data,” Results Math., vol. 62, nos. 3–4, pp. 337–354, 2012. [18] T. H. Cormen, Introduction to Algorithms. Cambridge, MA, USA: MIT Press, 2001. [19] M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. New York, NY, USA: Springer-Verlag, 2010. [20] R. R. Coifman and D. L. Donoho, “Translationinvariant de-noising,” Wavelets and Statistics. New York, NY, USA: Springer-Verlag, 1995, pp. 125–150. [21] T. Yang, Finite Element Structural Analysis, vol. 2. Englewood Cliffs, NJ, USA: Prentice-Hall, 1986. [22] D. Watson, Contouring: A Guide to the Analysis and Display of Spatial Data (With Programs on Diskette). New York, NY, USA: Pergamon, 1992.