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Entropy Based Parameter Estimation of 2D Gaussian Filter for Image Speckle Noise Removal | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 5، دوره 53، شماره 2، اسفند 2021، صفحه 189-200 اصل مقاله (4.65 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2021.19374.5389 | ||
| نویسندگان | ||
| Zahra Hosseini1؛ Mohammadreza Hassannejad Bibalan* 2 | ||
| 1Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology | ||
| 2Department of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran | ||
| چکیده | ||
| In this paper, a speckle noise suppression algorithm based on the 2D Gaussian filter is addressed, which employs entropy to estimate the filter's variance effectively. Speckle noise is an inherent characteristic of the coherent imaging systems, which degrades the quality of the resulting images. Gaussian filter is a traditional approach for speckle denoising. However, estimating its optimum variance is still a challenge. Many algorithms have been developed to estimate the optimum variance, but they suffer from the type of noise or a predetermined variance. Our proposed method demonstrates an improved 2D Gaussian filter since it estimates the optimum variance of the filter in the context of differential entropy between the noisy and filtered images under different -norms. This optimum variance is directly estimated from the speckle noise level of image and it differs for different types of noise and images. The optimization problem is numerically solved, and the value of the norm order is also appropriately determined. The blind estimation of norm order is also accomplished based on the level of noise variance. Finally, the proposed method's performance is appraised, utilizing both standard and real ultrasound (US) images. The quality of filtered images is assessed through the qualitative and quantitative simulations in terms of peak signal to noise ratio (PSNR), correlation coefficient (CoC), structural similarity (SSIM), and equivalent number of looks (ENL). The experimental results reveal the proposed method's proficiency in contradiction to state-of-the-art despeckling methods through the capability of strong speckle noise removal and preserving the edges and local features. | ||
| کلیدواژه‌ها | ||
| Image Denoising؛ 2D Gaussian Filter؛ Speckle Noise؛ Entropy | ||
| مراجع | ||
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[1] M. Ambrosanio, B. Kanoun, F. Baselice, wKSR-NLM: An Ultrasound Despeckling Filter Based on Patch Ratio and Statistical Similarity, IEEE Access, 8 (2020) 150773-150783. [2] H. Aetesam, S.K. Maji, Ultrasound Image Deconvolution adapted to Gaussian and Speckle Noise Statistics, in: 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, 2020, pp. 1114-1119. [3] W. Cui, M. Li, G. Gong, K. Lu, S. Sun, F. Dong, Guided trilateral filter and its application to ultrasound image despeckling, Biomedical Signal Processing and Control, 55 (2020) 101625. [4] L.H. Breivik, S.R. Snare, E.N. Steen, A.H.S. Solberg, Real-time nonlocal means-based despeckling, IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 64(6) (2017) 959-977. [5] K. Mei, B. Hu, B. Fei, B. Qin, Phase asymmetry ultrasound despeckling with fractional anisotropic diffusion and total variation, IEEE Transactions on Image Processing, 29 (2019) 2845-2859. [6] F. Mei, D. Zhang, Y. Yang, Improved non-local self-similarity measures for effective speckle noise reduction in ultrasound images, Computer Methods and Programs in Biomedicine, 196 (2020) 105670. [7] R. Lalchhanhima, D. Kandar, R. Chawngsangpuii, Single Look SAR Image Segmentation Using Local Entropy, Median Low-Pass Filter and Fuzzy Inference System, in: International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, Springer, 2020, pp. 149-159. [8] U. Erkan, S. Engınoğlu, D.N. Thanh, A recursive mean filter for image denoising, in: 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), IEEE, 2019, pp. 1-5. [9] S.I. Jabbar, C. Day, E. Chadwick, Automated reduction the speckle noise of the panoramic ultrasound images of Muscles and Tendons, in: Journal of Physics: Conference Series, IOP Publishing, 2020, pp. 012085. [10] N. Bodaisingi, B. Narayanam, Techniques for de‐noising of bio‐medical images, (2018). [11] M.H. Bibalan, H. Amindavar, Non-Gaussian amplitude PDF modeling of ultrasound images based on a novel generalized Cauchy-Rayleigh mixture, EURASIP Journal on Image and video Processing, 2016(1) (2016) 1-12. [12] Y. Tounsi, M. Kumar, A. Nassim, F. Mendoza-Santoyo, O. Matoba, Speckle denoising by variant nonlocal means methods, Applied optics, 58(26) (2019) 7110-7120. [13] D. Yin, Z. Gu, Y. Zhang, F. Gu, S. Nie, S. Feng, J. Ma, C. Yuan, Speckle noise reduction in coherent imaging based on deep learning without clean data, Optics and Lasers in Engineering, 133 (2020) 106151. [14] S. Saravani, R. Shad, M. Ghaemi, Iterative adaptive Despeckling SAR image using anisotropic diffusion filter and Bayesian estimation denoising in wavelet domain, Multimedia Tools and Applications, 77(23) (2018) 31469-31486. [15] F. Nar, SAR image despeckling using quadratic–linear approximated‐norm, Electronics Letters, 54(6) (2018) 387-389. [16] A. Gupta, V. Bhateja, A. Srivastava, A. Gupta, Suppression of speckle noise in ultrasound images using bilateral filter, in: Information and communication technology for intelligent systems, Springer, 2019, pp. 735-741. [17] Z. Zhou, E.Y. Lam, C. Lee, Nonlocal means filtering based speckle removal utilizing the maximum a posteriori estimation and the total variation image prior, IEEE Access, 7 (2019) 99231-99243. [18] W. Feng, H. Lei, Combination of geometric clustering and nonlocal means for SAR image despeckling, Electronics letters, 50(5) (2014) 395-396. [19] D. Gupta, R. Rathi, S. Gupta, An Efficient Approach of Filtering for Noises on Images, in: 2018 4th International Conference on Computing Communication and Automation (ICCCA), IEEE, 2018, pp. 1-5. [20] T. Tasnim, M.M.H. Shuvo, S. Hasan, Study of speckle noise reduction from ultrasound b-mode images using different filtering techniques, in: 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), IEEE, 2017, pp. 229-234. [21] Z. Hosseini, M.H. Bibalan, Speckle Noise Reduction Of Ultrasound Images Based On Neighbor Pixels Averaging, in: 2018 25th National and 3rd International Iranian Conference on Biomedical Engineering (ICBME), IEEE, 2018, pp. 1-6. [22] P. Kalavathi, M. Abinaya, S. Boopathiraja, Removal of Speckle Noise in Ultrasound Images using Spatial Filters, in: National Conference on Computational Methods, Communication Techniques and Informatics, 2017, pp. 174-177. [23] Z. Yu, W. Wang, C. Li, W. Liu, J. Yang, Speckle noise suppression in SAR images using a three-step algorithm, Sensors, 18(11) (2018) 3643. [24] X. Fu, Y. Wang, L. Chen, Y. Dai, Quantum-inspired hybrid medical ultrasound images despeckling method, Electronics Letters, 51(4) (2015) 321-323. [25] H. Seddik, A new family of Gaussian filters with adaptive lobe location and smoothing strength for efficient image restoration, EURASIP Journal on Advances in Signal Processing, 2014(1) (2014) 1-11. [26] Y. Jin, W. Jiang, J. Shao, J. Lu, An improved image denoising model based on nonlocal means filter, Mathematical Problems in Engineering, 2018 (2018). [27] D. Chan, J. Gambini, A.C. Frery, Speckle Noise Reduction In Sar Images Using Information Theory, in: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), IEEE, 2020, pp. 456-461. [28] M. Gupta, A. Garg, An efficient technique for speckle noise reduction in ultrasound images, in: 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, 2017, pp. 177-180. [29] S.K. Kopparapu, M. Satish, Identifying optimal Gaussian filter for Gaussian noise removal, in: 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, IEEE, 2011, pp. 126-129. [30] E. Rifkah, A. Amer, Automated Gaussian filtering VIA Gaussian scale space and linear diffusion, in: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), IEEE, 2012, pp. 1539-1542. [31] Y. Zhang, Y. Lu, Z. Zhang, J. Wang, C. He, T. Wu, Noise reduction by Brillouin spectrum reassembly in Brillouin optical time domain sensors, Optics and Lasers in Engineering, 125 (2020) 105865. [32] S. Yin, Y. Qian, M. Gong, Unsupervised hierarchical image segmentation through fuzzy entropy maximization, Pattern Recognition, 68 (2017) 245-259. [33] J. Prakash, S. Mandal, D. Razansky, V. Ntziachristos, Maximum entropy based non-negative optoacoustic tomographic image reconstruction, IEEE Transactions on Biomedical Engineering, 66(9) (2019) 2604-2616. [34] Y. Shen, Q. Liu, S. Lou, Y.-L. Hou, Wavelet-based total variation and nonlocal similarity model for image denoising, IEEE Signal Processing Letters, 24(6) (2017) 877-881. [35] L. Fabbrini, M. Greco, M. Messina, G. Pinelli, Improved anisotropic diffusion filtering for SAR image despeckling, Electronics letters, 49(10) (2013) 672-674. [36] H. Choi, J. Jeong, Speckle noise reduction technique for SAR images using statistical characteristics of speckle noise and discrete wavelet transform, Remote Sensing, 11(10) (2019) 1184. [37] J.L. Mateo, A. Fernández-Caballero, Finding out general tendencies in speckle noise reduction in ultrasound images, Expert systems with applications, 36(4) (2009) 7786-7797. [38] A.M. Molaei, M. Sedaaghi, H. Ebrahimnezhad, Steganography scheme based on Reed-Muller Code with improving payload and ability to Retrieval of Destroyed data for digital images, AUT Journal of Electrical Engineering, 49(1) (2017) 53-62. [39] U. Sara, M. Akter, M.S. Uddin, Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study, Journal of Computer and Communications, 7(3) (2019) 8-18. [40] F. Yaghmaee, E. Kalatehjari, Reduced-Reference Image Quality Assessment based on saliency region extraction, AUT Journal of Electrical Engineering, 51(1) (2019) 83-92. [41] S. Sahu, H.V. Singh, B. Kumar, A heavy-tailed levy distribution for despeckling ultrasound image, in: 2017 Fourth International Conference on Image Information Processing (ICIIP), IEEE, 2017, pp. 1-5. [42] A. Rodtook, K. Kirimasthong, W. Lohitvisate, S.S. Makhanov, Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities, Pattern Recognition, 79 (2018) 172-182. [43] F. Baselice, Ultrasound image despeckling based on statistical similarity, Ultrasound in medicine & biology, 43(9) (2017) 2065-2078. | ||
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