Comparison of Contourlet and Time-Invariant Contourlet Transform Performance for Different Types of Noises

Year 2019, Volume: 7 Issue: 4, 399 - 404, 30.10.2019

Muhammet Fatih Aslan Kadir Sabancı Akif Durdu

https://doi.org/10.17694/bajece.573583

Abstract

A noiseless image is
desirable for many applications. However, this is not possible. Generally, wavelet-based
methods are used to noise reduction. However, due to insufficient performance
of wavelet transforms (WT) on images, different multi-resolution analysis
methods have been proposed. In this study, one of them is Contourlet Transform
(CT) and the Translation-Invariant Contourlet Transform (TICT) which is an
improved version of CT is compared using different noises. The fundus images
are taken from the DRIVE dataset and benchmark images are used. Peak
Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean Structural
Similarity (MSSIM) and Feature Similarity Index (FSIM) are used as comparison
criteria. The results showed that TICT is better in Gaussian noisy images.

Keywords

Contourlet Transform, Image Denoising, Time Invariant Contourlet Transform

References

• [1] K. Panetta, S. Agaian, J.-C. Pinoli, and Y. Zhou, "Image processing algorithms and measures for the analysis of biomedical imaging systems applications," International journal of biomedical imaging, vol. 2015, 2015.
• [2] K. Leung, A. Cunha, A. W. Toga, and D. S. Parker, "Developing image processing meta-algorithms with data mining of multiple metrics," Computational and mathematical methods in medicine, vol. 2014, 2014.
• [3] E. Abele, L. Holland, and A. Nehrbass, "Image acquisition and image processing algorithms for movement analysis of bearing cages," Journal of Tribology, vol. 138, no. 2, p. 021105, 2016.
• [4] S. Ya-Lin and B. Chen-Xi, "Research and analysis of image processing technologies based on dotnet framework," Physics Procedia, vol. 25, pp. 2131-2137, 2012.
• [5] R. Patil, "Noise reduction using wavelet transform and singular vector decomposition," Procedia Computer Science, vol. 54, pp. 849-853, 2015.
• [6] A. Boyat and B. K. Joshi, "Image denoising using wavelet transform and median filtering," in Engineering (NUiCONE), 2013 Nirma University International Conference on, 2013, pp. 1-6: IEEE.
• [7] R. Sivakumar, G. Balaji, R. S. J. Ravikiran, R. Karikalan, and S. S. Janaki, "Image Denoising using Contourlet Transform," in 2009 Second International Conference on Computer and Electrical Engineering, 2009, vol. 1, pp. 22-25: IEEE.
• [8] D. Bhonsle, V. Chandra, and G. Sinha, "Medical image denoising using bilateral filter," International Journal of Image, Graphics and Signal Processing, vol. 4, no. 6, p. 36, 2012.
• [9] A. B. Hamza and H. Krim, "Image denoising: A nonlinear robust statistical approach," IEEE transactions on signal processing, vol. 49, no. 12, pp. 3045-3054, 2001.
• [10] A. Dixit and P. Sharma, "A Comparative Study of Wavelet Thresholding for Image Denoising," IJ Image, Graphics and Signal Processing, vol. 12, pp. 39-46, 2014.
• [11] N. Mehala and R. Dahiya, "A comparative study of FFT, STFT and wavelet techniques for induction machine fault diagnostic analysis," in Proceedings of the 7th WSEAS international conference on computational intelligence, man-machine systems and cybernetics, Cairo, Egypt, 2008, vol. 2931.
• [12] T. Bernas, E. K. Asem, J. P. Robinson, and B. Rajwa, "Compression of fluorescence microscopy images based on the signal‐to‐noise estimation," Microscopy research and technique, vol. 69, no. 1, pp. 1-9, 2006.
• [13] R. M. Willett and R. D. Nowak, "Platelets: a multiscale approach for recovering edges and surfaces in photon-limited medical imaging," IEEE Transactions on Medical Imaging, vol. 22, no. 3, pp. 332-350, 2003.
• [14] J. B. De Monvel, S. Le Calvez, and M. Ulfendahl, "Image restoration for confocal microscopy: improving the limits of deconvolution, with application to the visualization of the mammalian hearing organ," Biophysical Journal, vol. 80, no. 5, pp. 2455-2470, 2001.
• [15] P. V. Lavanya, C. V. Narasimhulu, and K. S. Prasad, "Transformations analysis for image denoising using complex wavelet transform," in Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017 International Conference on, 2017, pp. 1-7: IEEE.
• [16] P. Rakheja and R. Vig, "Image Denoising using Various Wavelet Transforms: A Survey," Indian Journal of Science and Technology, vol. 9, no. 48, 2017.
• [17] M. N. Do and M. Vetterli, "The contourlet transform: an efficient directional multiresolution image representation," IEEE Transactions on image processing, vol. 14, no. 12, pp. 2091-2106, 2005.
• [18] P. Hiremath, P. T. Akkasaligar, and S. Badiger, "Performance comparison of wavelet transform and contourlet transform based methods for despeckling medical ultrasound images," International Journal of Computer Applications, vol. 26, no. 9, pp. 34-41, 2011.
• [19] E. J. Candès and D. L. Donoho, "Ridgelets: A key to higher-dimensional intermittency?," Philosophical Transactions: Mathematical, Physical and Engineering Sciences, pp. 2495-2509, 1999.
• [20] E. J. Candes and D. L. Donoho, "Curvelets: A surprisingly effective nonadaptive representation for objects with edges," Stanford Univ Ca Dept of Statistics2000.
• [21] J. Xu, L. Yang, and D. Wu, "Ripplet: A new transform for image processing," Journal of Visual Communication and Image Representation, vol. 21, no. 7, pp. 627-639, 2010.
• [22] J. Krommweh, "Tetrolet transform: A new adaptive Haar wavelet algorithm for sparse image representation," Journal of Visual Communication and Image Representation, vol. 21, no. 4, pp. 364-374, 2010.
• [23] M. Ceylan and A. E. Canbilen, "Performance Comparison of Tetrolet Transform and Wavelet-Based Transforms for Medical Image Denoising," International Journal of Intelligent Systems and Applications in Engineering, vol. 5, no. 4, pp. 222-231, 2017.
• [24] R. Eslami and H. Radha, "Translation-invariant contourlet transform and its application to image denoising," IEEE Transactions on image processing, vol. 15, no. 11, pp. 3362-3374, 2006.
• [25] P. J. Burt and E. H. Adelson, "The Laplacian pyramid as a compact image code," in Readings in Computer Vision: Elsevier, 1987, pp. 671-679.
• [26] R. H. Bamberger and M. J. Smith, "A filter bank for the directional decomposition of images: Theory and design," IEEE transactions on signal processing, vol. 40, no. 4, pp. 882-893, 1992.
• [27] P. Han and J. Du, "Spatial images feature extraction based on bayesian nonlocal means filter and improved contourlet transform," Journal of Applied Mathematics, vol. 2012, 2012.
• [28] J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken, "Ridge-based vessel segmentation in color images of the retina," IEEE transactions on medical imaging, vol. 23, no. 4, pp. 501-509, 2004.