A COMPARISON STUDY FOR IMAGE DENOISING

Öz Image denoising is the detection and removal of outliers in a image. A measured analog signal is affected by both the device from which the measurement is performed and the noise from the environment. Various types of noise are available. With the developed noise reduction methods, it is tried to eliminate the existing noise. In this study, Bandelet Transform and Bilateral Filter denoising methods are compared. Both methods have been used to eliminate noise of different types and different rates added to the benchmark and retina images. Bandelet transform is performed for both hard and soft threshold. Peak Signal-to-Noise Ratio, Mean Squared Error, Mean Structural Similarity and Feature Similarity Index are used as a comparison method.

Kaynakça

[1] Buades, A., Coll, B., and Morel, J. M. (2004). On image denoising methods. Technical Note, CMLA (Centre de Mathematiques et de Leurs Applications), 5, pp. 1-40.

[2] Motwani, M. C., Gadiya, M. C., Motwani, R. C., and Harris, F. C. Survey of image denoising techniques." Proc., Proceedings of GSPX, pp. 27-30.

[3] Boyat, A., and Joshi, B. K. Image denoising using wavelet transform and median filtering. Proc., Engineering (NUiCONE), 2013 Nirma University International Conference on, IEEE, pp. 1-6.

[4] Buades, A., Coll, B., and Morel, J.-M. A non-local algorithm for image denoising. Proc., Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, IEEE, pp. 60-65.

[5] Portilla, J., Strela, V., Wainwright, M. J., and Simoncelli, E. P. (2003). Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Transactions on Image processing, 12(11), pp. 1338-1351.

[6] Luisier, F., Blu, T., and Unser, M. (2007). A new SURE approach to image denoising: Interscale orthonormal wavelet thresholding. IEEE Transactions on image processing, 16(3), pp. 593-606.

[7] Elad, M., and Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image processing, 15(12), pp. 3736-3745.

[8] Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K. (2007). Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on image processing, 16(8), pp. 2080-2095.

[9] Pu, Y.-F., Zhang, N., Zhang, Y., and Zhou, J.-L. (2016). A texture image denoising approach based on fractional developmental mathematics. Pattern Analysis and Applications, 19(2), pp. 427-445.

[10] Aggarwal, H. K., and Majumdar, A. (2016). Hyperspectral image denoising using spatio-spectral total variation. IEEE Geoscience and Remote Sensing Letters, 13(3), pp. 442-446.

[11] Lahmiri, S., and Boukadoum, M. (2015). A weighted bio-signal denoising approach using empirical mode decomposition. Biomedical Engineering Letters, 5(2), pp. 131-139.

[12] Buades, A., Coll, B., and Morel, J.-M. (2010). Image denoising methods. A new nonlocal principle. SIAM review, 52(1), pp. 113-147.

[13] Kaur, S., and Singh, N. (2014). Image Denoising Techniques: A Review. International Journal of Innovative Research in Computer and Communication Engineering, 2(6).

[14] Zhang, J., Zhang, H., Shi, X., and Geng, S. (2019). High Noise Astronomical Image Denoising via 2G-Bandelet Denoising Compressed Sensing. Optik.

[15] Wang, X., and Gao, J. Image Denoising Method Based on Nonsubsampled Contourlet Transform and Bandelet Transform. Proc., 2009 First International Conference on Information Science and Engineering, pp. 1278-1281.

[16] Hazavei, S. M., and Shahdoosti, H. R. (2017). Using Complex Wavelet Transform and Bilateral Filtering for Image Denoising. arXiv preprint arXiv:1702.01276.

[17] He, Y., Zheng, Y., Zhao, Y., Ren, Y., Lian, J., and Gee, J. (2017). Retinal Image denoising via bilateral filter with a spatial kernel of optimally oriented line spread function. Computational and mathematical methods in medicine, 2017.

[18] Ceylan, M., and Canbilen, A. E. (2017). Performance Comparison of Tetrolet Transform and Wavelet-Based Transforms for Medical Image Denoising. International Journal of Intelligent Systems and Applications in Engineering, 5(4), pp. 222-231.

[19] Le Pennec, E., and Mallat, S. (2005). Sparse geometric image representations with bandelets. IEEE transactions on image processing, 14(4), pp. 423-438.

[20] Villegas, O. O. V., Domínguez, H. d. J. O., and Sánchez, V. G. C. A comparison of the bandelet, wavelet and contourlet transforms for image denoising. Proc., Artificial Intelligence, 2008. MICAI'08. Seventh Mexican International Conference on, IEEE, pp. 207-212.

[21] Ashraf, R., Bashir, K., Irtaza, A., and Mahmood, M. T. (2015). Content based image retrieval using embedded neural networks with bandletized regions. Entropy, 17(6), pp. 3552-3580.

[22] Tomasi, C., and Manduchi, R. Bilateral filtering for gray and color images. Proc., Computer Vision, 1998. Sixth International Conference on, IEEE, pp. 839-846.

[23] Ahmed, S. S., Messali, Z., Ouahabi, A., Trépout, S., Messaoudi, C., and Marco, S. (2014). Bilateral Filtering and Wavelets based Image Denoising: Application to Electron Microscopy Images with Low Electron Dose. International Journal on Recent Trends in Engineering & Technology, 11(2), pp. 153-164.

[24] Kumar, B. S. (2013). Image denoising based on non-local means filter and its method noise thresholding. Signal, image and video processing, 7(6), pp. 1211-1227.

[25] Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., and Van Ginneken, B. (2004). Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging, 23(4), pp. 501-509.

Kaynak Göster

Bibtex @araştırma makalesi { ejt623068, journal = {European Journal of Technique (EJT)}, issn = {2536-5010}, eissn = {2536-5134}, address = {INESEG Yayıncılık Dicle Üniversitesi Teknokent, Sur/Diyarbakır}, publisher = {Hibetullah KILIÇ}, year = {2019}, volume = {9}, pages = {145 - 150}, doi = {10.36222/ejt.623068}, title = {A COMPARISON STUDY FOR IMAGE DENOISING}, key = {cite}, author = {Aslan, Muhammet Fatih and Durdu, Akif and Sabanci, Kadir} }
APA Aslan, M , Durdu, A , Sabanci, K . (2019). A COMPARISON STUDY FOR IMAGE DENOISING . European Journal of Technique (EJT) , 9 (2) , 145-150 . DOI: 10.36222/ejt.623068
MLA Aslan, M , Durdu, A , Sabanci, K . "A COMPARISON STUDY FOR IMAGE DENOISING" . European Journal of Technique (EJT) 9 (2019 ): 145-150 <https://dergipark.org.tr/tr/pub/ejt/issue/51266/623068>
Chicago Aslan, M , Durdu, A , Sabanci, K . "A COMPARISON STUDY FOR IMAGE DENOISING". European Journal of Technique (EJT) 9 (2019 ): 145-150
RIS TY - JOUR T1 - A COMPARISON STUDY FOR IMAGE DENOISING AU - Muhammet Fatih Aslan , Akif Durdu , Kadir Sabanci Y1 - 2019 PY - 2019 N1 - doi: 10.36222/ejt.623068 DO - 10.36222/ejt.623068 T2 - European Journal of Technique (EJT) JF - Journal JO - JOR SP - 145 EP - 150 VL - 9 IS - 2 SN - 2536-5010-2536-5134 M3 - doi: 10.36222/ejt.623068 UR - https://doi.org/10.36222/ejt.623068 Y2 - 2019 ER -
EndNote %0 European Journal of Technique A COMPARISON STUDY FOR IMAGE DENOISING %A Muhammet Fatih Aslan , Akif Durdu , Kadir Sabanci %T A COMPARISON STUDY FOR IMAGE DENOISING %D 2019 %J European Journal of Technique (EJT) %P 2536-5010-2536-5134 %V 9 %N 2 %R doi: 10.36222/ejt.623068 %U 10.36222/ejt.623068
ISNAD Aslan, Muhammet Fatih , Durdu, Akif , Sabanci, Kadir . "A COMPARISON STUDY FOR IMAGE DENOISING". European Journal of Technique (EJT) 9 / 2 (Aralık 2019): 145-150 . https://doi.org/10.36222/ejt.623068
AMA Aslan M , Durdu A , Sabanci K . A COMPARISON STUDY FOR IMAGE DENOISING. EJT. 2019; 9(2): 145-150.
Vancouver Aslan M , Durdu A , Sabanci K . A COMPARISON STUDY FOR IMAGE DENOISING. European Journal of Technique (EJT). 2019; 9(2): 145-150.
IEEE M. Aslan , A. Durdu ve K. Sabanci , "A COMPARISON STUDY FOR IMAGE DENOISING", European Journal of Technique (EJT), c. 9, sayı. 2, ss. 145-150, Ara. 2020, doi:10.36222/ejt.623068

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