Abstract
In this paper, a novel denoising approach based on optimal trilateral filtering using Grey Wolf Optimization (GWO) is proposed. At first, a database of noisy images are generated by adding Gaussian noise, Salt & Pepper noise and Random noise to the captured image. The filtering of noisy images are performed by Block-matching and 3D filtering (BM3D) algorithm over the components of image obtained through the moving frame approach. Then, using optimal trilateral filtering, the denoised images are reconstructed. Therefore, by using a two-level filtering approach such as Moving frame-based Block-matching and 3D filtering (BM3D) and Optimal trilateral filtering the noisy images are decomposed. The proposed optimal trilateral filter employs Grey Wolf Optimization algorithm for selecting the parameters optimally to improve the efficiency of filtering method which also reduces the time required for manual computation. The performance of the proposed image denoising algorithm is analyzed using multiple datasets and the analysis of results were done in contrast with existing conventional approaches. The results validated that the optimal trilateral filtering approach outperforms other conventional methods in terms of Mean-Square Error (MSE) and the Peak Signal-to-Noise Ratio (PSNR).
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References
Aravindan TE, Seshasayanan R (2018) Denoising brain images with the aid of discrete wavelet transform and monarch butterfly optimization with different noises. J Med Syst 42(11):207
Barbu T (2016) A hybrid nonlinear fourth-order PDE-based image restoration approach. System Theory, Control and Computing (ICSTCC), 2016 20th International Conference on. IEEE
Chang HH (2010). Entropy-based trilateral filtering for noise removal in digital images. In: 2010 3rd International Congress on Image and Signal Processing (vol. 2, pp 673–677). IEEE
Chato L, Latifi S, Kachroo P (2017) Total variation denoising method to improve the detection process in IR images. Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), 2017 IEEE 8th annual. IEEE
Chen Y, Pock T (2016) Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans Pattern Anal Mach Intell 39(6):1256–1272
Chen Q, Wu D (2010) Image denoising by bounded block matching and 3D filtering. Signal Process 90(9):2778–2783
Choudhury P, Tumblin J (2005) The trilateral filter for high contrast images and meshes. In: ACM SIGGRAPH 2005 courses (pp. 5-es)
Cruz C et al (2018) Nonlocality-reinforced convolutional neural networks for image denoising. arXiv preprint arXiv:1803.02112
Dai T, Lu W, Wang W, Wang J, Xia S-T (2017) Entropy-based bilateral filtering with a new range kernel. Signal Process 137:223–234
Dey MT et al (2016) An efficient hardware accelerated design for image denoising using Extended Trilateral Filter. Control, Instrumentation, Energy & Communication (CIEC), 2016 2nd International Conference on. IEEE
Ghimpeţeanu G et al (2016) A decomposition framework for image denoising algorithms. IEEE Trans Image Process 25(1):388–399
Gu S, Xie Q, Meng D, Zuo W, Feng X, Zhang L (2017) Weighted nuclear norm minimization and its applications to low level vision. Int J Comput Vis 121(2):183–208
Guo Q, Zhang C, Zhang Y, Liu H (2016) An efficient SVD-based method for image denoising. IEEE Trans Circ Syst Video Technol 26(5):868–880
Hsieh P-W, Shao P-C, Yang S-Y (2018) A regularization model with adaptive diffusivity for variational image denoising. Signal Process 149:214–228
Hu H, Froment J, Liu Q (2018) A note on patch-based low-rank minimization for fast image denoising. J Vis Commun Image Represent 50:100–110
Joseph J, Periyasamy R (2018) An image driven bilateral filter with adaptive range and spatial parameters for denoising magnetic resonance images. Comput Electr Eng 69:782–795
Kim JH, Akram F, Choi KN (2017) Image denoising feedback framework using split Bregman approach. Expert Syst Appl 87:252–266
Kumar A, Ahmad MO, Swamy MNS (2019) A framework for image denoising using first and second order fractional overlapping group sparsity (HF-OLGS) regularizer. IEEE Access 7:26200–26217
Lee D, Choi S, Kim H-J (2018) Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography. Nucl Instrum Methods Phys Res A Accelerat Spectrom Detect Assoc Equip 884:97–104
Li YJ, Zhang J, Wang M (2017) Improved BM3D denoising method. IET Image Process 11(12):1197–1204
Mansoor A, Bagci U, Mollura DJ (2014) Optimally stabilized PET image denoising using trilateral filtering. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp 130–137
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Onuki M, Ono S, Yamagishi M, Tanaka Y (2016) Graph signal denoising via trilateral filter on graph spectral domain. IEEE Trans Signal Inf Proc Networks 2(2):137–148
Phophalia A, Mitra SK (2015) Rough set based bilateral filter design for denoising brain MR images. Appl Soft Comput 33:1–14
Rafsanjani HK, Sedaaghi MH, Saryazdi S (2017) An adaptive diffusion coefficient selection for image denoising. Digit Signal Proc 64:71–82
Rejeesh MR (2019) Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed Tools Appl 1–20
Singh K, Ranade SK, Singh C (2017) Comparative performance analysis of various wavelet and nonlocal means-based approaches for image denoising. Optik – Int J Light Electron Optics 131:423–437
Trinh D-H, Luong M, Dibos F, Rocchisani J-M, Pham C-D, Nguyen TQ (2014) Novel example-based method for superresolution and denoising of medical images. IEEE Trans Image Process 23(4):1882–1895
Verma R, Pandey R (2017) Adaptive selection of search region for NLM based image denoising. Optik-International Journal for Light and Electron Optics 147:151–162
Wong WC, Chung AC, Yu SC (2004) Trilateral filtering for biomedical images. In: 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821) (pp. 820-823). IEEE
Yang S, Shi Z (2016) Hyperspectral image target detection improvement based on total variation. IEEE Trans Image Process 25(5):2249–2258
Zhang M, Desrosiers C (2018) Structure preserving image denoising based on low-rank reconstruction and gradient histograms. Comput Vis Image Underst 171:48–60
Zhang Y, Tian X, Ren P (2014) An adaptive bilateral filter-based framework for image denoising. Neurocomputing 140:299–316
Zhang Y et al (2018) Kernel Wiener filtering model with low-rank approximation for image denoising. Inf Sci 462. https://doi.org/10.1016/j.ins.2018.06.028
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Rejeesh M R, Thejaswini P MOTF: Multi-objective Optimal Trilateral Filtering based partial moving frame algorithm for image denoising. Multimed Tools Appl 79, 28411–28430 (2020). https://doi.org/10.1007/s11042-020-09234-5
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DOI: https://doi.org/10.1007/s11042-020-09234-5