An Edge Preserving Image Denoising Framework Based on Statistical Edge Detection and Bilateral Filter

Yıl 2021, , 519 - 531, 31.12.2021

Ebru Aydogan Duman

https://doi.org/10.29048/makufebed.1029276

Cited By: 1

Öz

The task of reducing noise from an image is known as image denoising. Although there are various methods and algorithms proposed in the literature, the methods still have limitations. The approaches generally either fail to reduce noise adequately or cause to be lost while effectively reducing noise. Conventional methods have poor performance when considering the success of preserving region boundaries and small structures. Conversely, modern techniques are more effective to smooth images without over smoothing edge details. To address these deficiencies and benefits, in this paper, we aim to develop a framework, which is capable of detecting whether a pixel is a part of edges or textures in an image so the framework can decide which filter should be used depending on region information. The Rank Order Test Method is used to detect image edges. In this way, we determine both which neighbors should be included to build a filter mask in the calculation for each pixel and which filter method should be implemented. We have compared the performance of Bilateral Filter-based methods. Experiments demonstrate that the proposed framework outperforms in terms of both PSNR, SSIM and visual perception for the noise with standard deviations 10, 30, 50. While the average PSNR value was 30.33 DB for the proposed model, the method with the closest result achieved an average score of 28.33 DB.

Anahtar Kelimeler

Bilateral filter, image denoising, region growing segmentation, statistical test

İstatistiksel Kenar Algılama ve Bilateral Filtreye Dayalı Kenar Korumalı Gürültü Giderme Yöntemi

Öz

Bir görüntüdeki gürültüyü azaltma işlemi, gürültü giderme olarak adlandırılır. Literatürde önerilen çeşitli yöntemler ve algoritmalar olmasına rağmen, yöntemlerin hala sınırlamaları bulunmaktadır. Yaklaşımlar genellikle ya gürültüyü yeterince azaltmakta başarısız olur ya da gürültüyü etkili bir şekilde azaltırken görüntünün kaybolmasına neden olur. Bölge sınırlarını ve küçük yapıları korumanın başarısı göz önüne alındığında, geleneksel yöntemlerin performansı düşüktür. Tersine, modern teknikler, kenar ayrıntılarını aşırı yumuşatmadan görüntüleri düzeltmek için daha etkilidir. Bu eksiklikleri ve faydaları göz önünde bulundurarak, bu çalışmada, bir pikselin bir görüntüdeki kenarların mı yoksa dokuların bir parçası mı olduğunu tespit edebilen ve böylece çerçevenin bölge bilgisine bağlı olarak hangi filtrenin kullanılması gerektiğine karar verebilen bir çerçeve geliştirilmesi amaçlanmıştır. Sıralama Testi Yöntemi, görüntü kenarlarını tespit etmek için kullanılır. Bu sayede her piksel için yapılan hesaplamada filtre maskesi oluşturmak için hangi komşuların dâhil edilmesi gerektiği hem de hangi filtre yönteminin uygulanması gerektiği belirlenmiştir. Çalışmada Bilateral Filtre tabanlı yöntemlerin performanslarını karşılaştırılmıştır Deneyler, 10,30 ve 50 standart sapmalara sahip gürültüler için önerilen çerçevenin PSNR, SSIM ve görsel algı açısından daha iyi performans sağladığını göstermektedir. Ortalama PSNR değeri 30.33 DB iken, en yakın sonuca sahip olan yöntem 28.33 DB ortalama puan elde etmiştir.

Anahtar Kelimeler

Bilateral filtre, gürültü giderme, alan büyütmeli bölütleme, istatistiksel test

Kaynakça

• Bargshady, G., Zhou, X., Deo, R.C., Soar, J., Whittaker, F., Wang, H. (2020). Enhanced deep learning algorithm development to detect pain intensity from facial expression images. Expert Systems with Applications, 149, 113305; DOI: https://doi.org/10.1016/j.eswa.2020.113305
• Benesty, J., Chen, J., Huang, Y.A., Doclo, S. (2005). Study of the Wiener filter for noise reduction. In: Speech enhancement, 9-41, Springer, Berlin, Heidelberg.
• Buades, A., Coll, B., Morel, J. M. (2005). A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) 2:60-65.
• Candes, E., Demanet, L., Donoho, D., Ying, L. (2006). Fast discrete curvelet transforms. Multiscale modeling & simulation, 5(3): 861-899.
• Chaudhury, K.N., Rithwik, K. (2015). Image denoising using optimally weighted bilateral filters: A sure and fast approach. In: 2015 IEEE International Conference on Image Processing (ICIP), 108-112.
• Chen, B.H., Tseng, Y.S., Yin, J.L. (2020). Gaussian-adaptive bilateral filter. IEEE Signal Processing Letters, 27: 1670-1674.
• Chen, G.Y., Bui, T.D., Krzyżak, A. (2005). Image denoising with neighbour dependency and customized wavelet and threshold. Pattern recognition, 38(1): 115-124.
• Cho, H., Lee, H., Kang, H., Lee, S. (2014). Bilateral texture filtering. ACM Transactions on Graphics (TOG), 33(4): 1-8.
• Dengwen, Z., Wengang, C. (2008). Image denoising with an optimal threshold and neighbouring window. Pattern Recognition Letters, 29(11): 1694-1697.
• Donoho, D.L. (1995). De-noising by soft-thresholding. IEEE transactions on information theory, 41(3): 613-627.
• Duman, E., Erdem, O.A. (2017). A new image denoising method based on region growing segmentation. In: 2017 25th Signal Processing and Communications Applications Conference (SIU), 1-4.
• Duman, E., Erdem, O.A. (2018). A statistical edge detection framework for noisy images. In: 2018 26th Signal Processing and Communications Applications Conference (SIU), 1-4.
• Frosio, I., Egiazarian, K., Pulli, K. (2015). Machine learning for adaptive bilateral filtering. In: Image Processing: Algorithms and Systems XIII (Vol. 9399, p. 939908). International Society for Optics and Photonics; DOI: https://doi.org/10.1117/12.2077733
• Gerig, G., Kubler, O., Kikinis, R., Jolesz, F.A. (1992). Nonlinear anisotropic filtering of MRI data. IEEE Transactions on medical imaging, 11(2): 221-232.
• Gonzalez, R.C. (2016). Digital image processing. In: Prentice hall.
• Hong, C., Yu, J., Zhang, J., Jin, X., Lee, K.H. (2018). Multimodal face-pose estimation with multitask manifold deep learning. IEEE Transactions on Industrial Informatics, 15(7): 3952-3961. Hong, J.H., Cho, S.B., Cho, U.K. (2009). A novel evolutionary approach to image enhancement filter design: method and applications. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(6): 1446-1457.
• Hoon Lim, D. (2006). Robust rank-order test for edge detection in noisy images. Nonparametric Statistics, 18(3): 333-342.
• Huynh-Thu, Q., Ghanbari, M. (2008). Scope of validity of PSNR in image/video quality assessment. Electronics letters, 44(13): 800-801.
• Jain, P., Tyagi, V. (2016). A survey of edge-preserving image denoising methods. Information Systems Frontiers, 18(1): 159-170.
• Lim, D.H. (2006). Robust edge detection in noisy images. Computational Statistics & Data Analysis, 50(3): 803-812.
• Routray, S., Ray, A.K., Mishra, C. (2018). Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform. Optik, 159: 333-343.
• Selesnick, I.W., Parekh, A., Bayram, I. (2014). Convex 1-D total variation denoising with non-convex regularization. IEEE Signal Processing Letters, 22(2): 141-144.
• Shao, L., Yan, R., Li, X., Liu, Y. (2013). From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms. IEEE transactions on cybernetics, 44(7): 1001-1013. Shen, Y., Liu, Q., Lou, S., Hou, Y.L. (2017). Wavelet-based total variation and nonlocal similarity model for image denoising. IEEE Signal Processing Letters, 24(6): 877-881.
• Singh, K., Ranade, S.K., Singh, C. (2017). Comparative performance analysis of various wavelet and nonlocal means based approaches for image denoising. Optik, 131: 423-437.
• Srivastava, A., Bhateja, V., Tiwari, H. (2015). Modified anisotropic diffusion filtering algorithm for MRI. In: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom) , 1885-1890.
• Starck, J.L., Candès, E.J., Donoho, D.L. (2002). The curvelet transform for image denoising. IEEE Transactions on image processing, 11(6): 670-684.
• Tomasi, C., Manduchi, R. (1998). Bilateral filtering for gray and color images. In: 6th International Conference on Computer Vision (IEEE Cat. No. 98CH36271), 839-846.
• Ünver, H.M., Kökver, Y., Duman, E., Erdem, O.A. (2019). Statistical edge detection and circular hough transform for optic disk localization. Applied Sciences, 9(2), 350.
• Verma, O.P., Hanmandlu, M., Susan, S., Kulkarni, M., Jain, P.K. (2011). A simple single seeded region growing algorithm for color image segmentation using adaptive thresholding. In: 2011 International Conference on Communication Systems and Network Technologies, 500-503.
• Wang, L., Wei, L.Y., Zhou, K., Guo, B., Shum, H.Y. (2007). High Dynamic Range Image Hallucination. In: Rendering Techniques, 321-326.
• Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600-612.
• Weickert, J. (1998). Anisotropic diffusion in image processing. Teubner Stuttgart.
• Zhang, B., Allebach, J.P. (2008). Adaptive bilateral filter for sharpness enhancement and noise removal. IEEE transactions on Image Processing, 17(5): 664-678.