Advancing Three-Stage Impulse Image Noise Removal Methodology via Neural Networks
Subject Areas : Electronic
1 - استادیار، گروه مهندسی صنایع، واحد سمنان، دانشگاه آزاد اسلامی، سمنان، ایران
Keywords: Impulse Noise, Image Processing, Neural Networks, Salt and Pepper Noise,
Abstract :
Image performance often faces jeopardization due to the presence of undesired noise. Among the various forms of noise contamination, impulse noise remains a significant concern for digital images. Two primary categories of impulse noise are recognized: salt and pepper noise, and random valued noise. Salt and pepper noise perturbs images by causing individual corrupted pixels to adopt either the maximum or minimum gray level value.This study presents a novel approach for the identification and restoration of Salt and Pepper Image Impulse Noises, employing a three-tiered framework utilizing distinct Trained Artificial Neural Networks (ANNs). The noise detection phase addresses two distinct categories of salt and pepper noises: White Range Noises and Black Range Noises. The initial artificial neural network (ANN-I) is dedicated to the identification of white range noises within images afflicted by salt and pepper noise. Subsequently, the second trained artificial neural network (ANN-II) is employed for the restoration of the previously detected white range noises. In the final stage, attention is directed toward detecting remaining black range noises and their subsequent restoration through the utilization of ANN-II, which has been specifically trained to rectify damaged pixels. To evaluate the efficacy of the proposed algorithm, two critical performance metrics are employed: Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE). These metrics are computed and subsequently compared against those produced by established filters for a variety of test cases.
[1] P. L. B. Soares, J. P. Da Silvia, “Neural Networks Applied for impulse Noise Reduction from Digital Images,” INFOCOMP, vol. 11, no. 3-4, pp. 07-14, 2012.
[2] S.G. Chang, Bin Yu, M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE transactions on image processing, vol. 9, no. 9, pp. 1532-1546, 2000.
[3] M. H. Hsieh, F. C. Cheng, M. C. Shie, S. J. Ruan, “Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images,” Engineering Applications of Artificial Intelligence, vol. 26, no. 4, pp. 1333-1338, 2013.
[4] R. C. Gonzalez, R. E. Woods, S. L. Eddins, “Eddins, Digital Image Processing Using MATLAB,” Third New Jersey: Prentice Hall, 2004.
[5] A. R. Forouzan, B. N. Araabi, “Iterative median filtering for restoration of images with impulsive noise,” in Electronics, Circuits and Systems, 2003. ICECS 2003. Proceedings of the 2003 10th IEEE International Conference on, vol. 1, pp. 232-235.
[6] D. Zhang, Z. Wang, “Impulse noise detection and removal using fuzzy techniques,” Electronics letters, vol. 33, no. 5, pp. 378-379, 1997.
[7] S. Masiero, M. Armani, G. Ferlini, G. Rosati and A. Rossi, “Randomized trial of a robotic assistive device for the upper extremity during early inpatient stroke rehabilitation,” IEEE Transactions on Neural Networks, vol. 12, no. 3, pp. 516-531, 2001.
[8] P. Civicioglu, “Using uncorrupted neighborhoods of the pixels for impulsive noise suppression with ANFIS,” IEEE Transactions on Image Processing, vol. 13, no. 3, p. 759-773, 2007.
[9] H. Ibrahim, N. S. P Kong, T. F. Ng, “Simple adaptive median filter for the removal of impulse noise from highly corrupted images,” IEEE Transactions on Consumer Electronics, vol. 54, no. 4, pp. 1920-1927, 2008.
[10] E. Abreu, M. Lightstone, S.K. Mitra, K. Arakawa, “A new efficient approach for the removal of impulse noise from highly corrupted images,” IEEE Transactions on Image Processing, vol. 5, no. 6, pp. 1012-1025, 1996.
[11] H. Xu, G. Zhu, H. Peng, D. Wang, “Adaptive fuzzy switching filter for images corrupted by impulse noise,” Pattern Recognition Letters, vol. 25, no. 15, pp. 1657-1663, 2004.
[12] M. E. Yuksel, “A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise,” IEEE Transactions on Image Processing, vol. 15, no. 4, pp. 928-936, 2006.
[13] A. Chinnapalanichamy, A. D. R. Singh, K. N. Ajith, “A morphological approach to remove salt and pepper noise in images,” International Journal of Computer Technology and Applications, vol. 3, no. 6, pp. 1875-1880, 1875.
[14] T. Nodes, N. Gallagher, “Median filters: Some modifications and their properties,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 30, no. 5, pp. 739-746, 1982.
[15] C. Wang, T. Chen, Z. Qu , “A novel improved median filter for salt-and-pepper noise from highly corrupted images,” in Systems and Control in Aeronautics and Astronautics (ISSCAA), 2010 3rd International Symposium on. IEEE, pp. 718-722.
[16] Z Wang, D Zhang, “Progressive switching median filter for the removal of impulse noise from highly corrupted images,” IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 46, no. 1, pp. 78-80, 1999.
[17] H. C. Chen, W. J. Wang, “Efficient impulse noise reduction via local directional gradients and fuzzy logic,” Fuzzy sets and systems, vol. 160, no. 13, pp. 1841-1857, 2009.
[18] G. Kaliraj, S. Baskar, “An efficient approach for the removal of impulse noise from the corrupted image using neural network based impulse detector,” Image and Vision Computing, vol. 28, no. 3, pp. 458-466, 2010.
[19] R. C. Gonzalez, R. E. Woods, “ Digital image processing second edition (3rd Edition),” Pearson, ISBN-13: 978-0131687288, 2007.
[20] Aslam, Muhammad. "Removal of the Noise & Blurriness using Global & Local Image Enhancement Equalization Techniques." International Journal of Computational and Innovative Sciences 1.1 (2022).
[21] Zheng, Yu-Bang, et al. "Mixed noise removal in hyperspectral image via low-fibered-rank regularization." IEEE Transactions on Geoscience and Remote Sensing 58.1 (2019): 734-749.
[22] Fan, Linwei, et al. "Brief review of image denoising techniques." Visual Computing for Industry, Biomedicine, and Art 2.1 (2019): 1-12.
[23] Zin, Theingi, Takuro Yamaguchi, and Masaaki Ikehara. "Mixed Noise Removal of Image with Interpolation." IEEJ Transactions on Electronics, Information and Systems 140.8 (2020): 1010-1018.
[24] Thanh, Dang Ngoc Hoang, and Serdar Engínoğlu. "An iterative mean filter for image denoising." IEEE Access 7 (2019): 167847-167859.
[25] Charmouti, Bilal, et al. "A new denoising method for removing salt & pepper noise from image." Multimedia Tools and Applications (2022): 1-13.