Extension and Analysis of the ARG algorithm to 2D
Keywords:
Adaptive Filter, 2D, Noise Cancellation, 2DARγ, TD, TWD.Abstract
Two-dimensional (2D) adaptive filtering is a technique that has been used for denoising in applications such as biomedical image processing in recent years. In this paper, we design the extension of the 1D-ARG adaptive filtering schemes to form new 2D-ARG adaptive filters. To compare the performance of the proposed algorithm in noise reduction in digital images, three images of different sizes from the Matlab library, Moon, Pout, and Cameraman, are taken as reference. This reduction is compared with two other gradient algorithms least mean squares (LMS) and normalized least mean squares (NLMS) for 2D adaptive filter design. Based on the simulation results and the established metrics, we demonstrate that the proposed method achieves a noise reduction eventually superior to the other 2D gradient algorithms.
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References
N. Sasaoka, K. Shimada, S. Sonobe, Y. Itoh, and K. Fujii, “Speech
enhancement based on adaptive filter with variable step size for wideband and periodic noise,” in 2009 52nd IEEE International Midwest
Symposium on Circuits and Systems, pp. 648–652, IEEE, 2009.
M. S. Ahmad, O. Kukrer, and A. Hocanin, “A 2-d recursive inverse
adaptive algorithm,” Signal, Image and Video Processing, vol. 7, no. 2,
pp. 221–226, 2013.
P. U. Kim, Y. Lee, J. H. Cho, and M. N. Kim, “Modified adaptive
noise canceller with an electrocardiogram to enhance heart sounds in
the auscultation sounds,” Biomedical Engineering Letters, vol. 1, no. 3,
p. 194, 2011.
W. W. A.J. Wright, “Adverse effects of vancomycin administered in the
perioperative period,” in Mayo Clinic Proceedings, vol. 61, pp. 721–724,
Elsevier, 1986.
Y. Liu, Z. Gui, and Q. Zhang, “Noise reduction for low-dose xray ct based on fuzzy logical in stationary wavelet domain,” OptikInternational Journal for Light and Electron Optics, vol. 124, no. 18,
pp. 3348–3352, 2013.
L. Wang, J. Lu, Y. Li, T. Yahagi, and T. Okamoto, “Noise removal
for medical x-ray images in wavelet domain,” Electrical Engineering in
Japan, vol. 163, no. 3, pp. 37–46, 2008.
J.-K. Park, S.-H. Kang, M. Park, D. Lee, K. Kim, and Y. Lee,
“Noise level and similarity evaluations of non-local means algorithm
in chest digital tomosynthesis x-ray imaging system: An experimental
study,” Nuclear Instruments and Methods in Physics Research Section
A: Accelerators, Spectrometers, Detectors and Associated Equipment,
vol. 1029, p. 166404, 2022.
X. He, C. Wang, R. Zheng, Z. Sun, and X. Li, “Gpr image denoising with
nsst-unet and an improved bm3d,” Digital Signal Processing, vol. 123,
p. 103402, 2022.
C. Rajesh and S. Kumar, “An evolutionary block based network for
medical image denoising using differential evolution,” Applied Soft
Computing, vol. 121, p. 108776, 2022.
Y. Zhang, W. Li, L. Zhang, X. Ning, L. Sun, and Y. Lu, “Agcnn:
adaptive gabor convolutional neural networks with receptive fields for
vein biometric recognition,” Concurrency and Computation: Practice
and Experience, p. e5697, 2020.
M. S. E. Abadi and S. N. Aali, “The novel two-dimensional adaptive
filter algorithms with the performance analysis,” Signal processing,
vol. 103, pp. 348–366, 2014.
M. M. Hadhoud and D. W. Thomas, “The two-dimensional adaptive lms
(tdlms) algorithm,” IEEE transactions on circuits and systems, vol. 35,
no. 5, pp. 485–494, 1988.
B. Widrow, J. R. Glover, J. M. McCool, J. Kaunitz, C. S. Williams,
R. H. Hearn, J. R. Zeidler, J. E. Dong, and R. C. Goodlin, “Adaptive
noise cancelling: Principles and applications,” Proceedings of the IEEE,
vol. 63, no. 12, pp. 1692–1716, 1975.
S. Kockanat and N. Karaboga, “A novel 2d-abc adaptive filter algorithm:
a comparative study,” Digital Signal Processing, vol. 40, pp. 140–153,
B. Gupta and A. R. Verma, “A novel approach of 2d adaptive filter based
on mpso technique for biomedical image,” Augmented Human Research,
vol. 5, no. 1, pp. 1–8, 2020.
P. K. Gupta, S. Lal, and F. Husain, “Artificial bee colony optimization
based despeckling framework for ultrasound images.,” Journal of Engineering Science & Technology Review, vol. 13, no. 5, 2020.
P. E. Jojoa Gómez, Um Algoritmo Acelerador de Parametros. PhD
thesis, Escola Politécnica da Universidade de Sao Paulo, 2003.
F. M. Pait, “A tuner that accelerates parameters,” Systems & Control
Letters, vol. 35, no. 1, pp. 65–68, 1998.
A. M. S. ESFAND and S. Nikbakht, “Image denoising with twodimensional adaptive filter algorithms,” 2011.
R. C. Gonzalez and R. E. Woods, “Digital image processing, prentice
hall,” Upper Saddle River, NJ, 2008.
T. B. Chandra and K. Verma, “Analysis of quantum noise-reducing filters
on chest x-ray images: A review,” Measurement, vol. 153, p. 107426,