De-noising of an Image using Fuzzy Inference System and Performance Comparison with the Conventional system

Ahmed  Farhan; Rezwan us Saleheen; Chen Li  Wei; Farhan  Mahbub

doi:10.38032/jea.2021.03.007

Authors

Ahmed Farhan College of Information and Communication Engineering, Harbin Engineering University, Heilongjiang, China
Rezwan us Saleheen Department of Mechatronics Engineering, World University of Bangladesh, Dhaka, Bangladesh
Chen Li Wei College of Information and Communication Engineering, Harbin Engineering University, Heilongjiang, China
Farhan Mahbub Department of Mechatronics Engineering, World University of Bangladesh, Dhaka, Bangladesh

DOI:

https://doi.org/10.38032/jea.2021.03.007

Keywords:

Fuzzy Logic, Fuzzy Inference System, Salt & Pepper Noise, PSNR, MSE

Abstract

Noise prevailing in the image can diminish the physical appearance of the objects existing within the image and make them frail. Present research emphasizes a fuzzy inference system eradicating several types of noise from the images. The investigation implies the utilization of different levels of Salt & Pepper noise. Followed by the pixel determination applying a mask, the disparity between the focused pixel's intensity with the minimum, average, and maximum power of the chosen window has been determined. Since two fuzzy valued outputs have been obtained to match them, the one provided by a low noise rate would demonstrate the more accurate filter for the selected window. Utilizing Matlab the Peak Signal-to-Noise ratio (PSNR) and Mean Square Error (MSE) are determined for evaluating the noise reduction performance. However, these values of PSNR and MSE obtained from this research are also compared with the conventional fuzzy filtering system.

References

Blanes-Vidal, V., Cantuaria, M.L. and Nadimi, E.S., 2017. A novel approach for exposure assessment in air pollution epidemiological studies using neuro-fuzzy inference systems: Comparison of exposure estimates and exposure-health associations. Environmental research, 154, pp.196-203. DOI: https://doi.org/10.1016/j.envres.2016.12.028

Godil, S.S., Shamim, M.S., Enam, S.A. and Qidwai, U., 2011. Fuzzy logic: A “simple” solution for complexities in neurosciences?. Surgical neurology international, 2. DOI: https://doi.org/10.4103/2152-7806.77177

Farhan, A., Wei, C.L. and Ahmed, M.T., 2018. A Qualitative Overview of Fuzzy Logic in ECG Arrhythmia Classification. International Journal of Engineering Works, 5(11), pp. 232-239.

Mohebbian, M.R., Hassan, A.M., Wahid, K.A. and Babyn, P., 2020, June. Multi-Frame Low-Dose CT Image noise reduction using Adaptive Type-2 Fuzzy filter and Fast-ICA. In 2020 IEEE Region 10 Symposium (TENSYMP) (pp. 690-693). IEEE. DOI: https://doi.org/10.1109/TENSYMP50017.2020.9230723

Altundogan, T.G. and Karakose, M., 2020, May. A Noise Reduction Approach Using Dynamic Fuzzy Cognitive Maps for Vehicle Traffic Camera Images. In 2020 Zooming Innovation in Consumer Technologies Conference (ZINC) (pp. 15-20). IEEE. DOI: https://doi.org/10.1109/ZINC50678.2020.9161438

Mahalakshmi, T. and Sreenivas, A., 2020. Adaptive Filter with Type-2 Fuzzy System and Optimization-Based Kernel Interpolation for Satellite Image Denoising. The Computer Journal, 63(6), pp.913-926. DOI: https://doi.org/10.1093/comjnl/bxz168

Golshan, H. and Hasanzadeh, R.P., 2021. Fuzzy Hysteresis Smoothing: A New Approach for Image Denoising. IEEE Transactions on Fuzzy Systems, 29(3), pp.686-697. DOI: https://doi.org/10.1109/TFUZZ.2019.2961336

Saadia, A. and Rashdi, A., 2016. Fractional order integration and fuzzy logic based filter for denoising of echocardiographic image. Computer methods and programs in biomedicine, 137, pp.65-75. DOI: https://doi.org/10.1016/j.cmpb.2016.09.006

Babu, J.J.J. and Sudha, G.F., 2016. Adaptive speckle reduction in ultrasound images using fuzzy logic on Coefficient of Variation. Biomedical Signal Processing and Control, 23, pp.93-103. DOI: https://doi.org/10.1016/j.bspc.2015.08.001

Ananthi, V.P. and Balasubramaniam, P., 2016. A new image denoising method using interval-valued intuitionistic fuzzy sets for the removal of impulse noise. Signal Processing, 121, pp.81-93. DOI: https://doi.org/10.1016/j.sigpro.2015.10.030

Wang, G., Zhu, H. and Wang, Y., 2015. Fuzzy decision filter for color images denoising. Optik, 126(20), pp.2428-2432. DOI: https://doi.org/10.1016/j.ijleo.2015.06.005

Wang, G., Liu, Y., Xiong, W. and Li, Y., 2018. An improved non-local means filter for color image denoising. Optik, 173, pp.157-173. DOI: https://doi.org/10.1016/j.ijleo.2018.08.013

Zhang, Y., Xu, S., Chen, K., Liu, Z. and Chen, C.P., 2016. Fuzzy density weight-based support vector regression for image denoising. Information Sciences, 339, pp.175-188. DOI: https://doi.org/10.1016/j.ins.2016.01.007

Singh, V., Dev, R., Dhar, N.K., Agrawal, P. and Verma, N.K., 2018. Adaptive type-2 fuzzy approach for filtering salt and pepper noise in grayscale images. IEEE transactions on fuzzy systems, 26(5), pp.3170-3176. DOI: https://doi.org/10.1109/TFUZZ.2018.2805289

Farooque, M.A. and Rohankar, J.S., 2013. Survey on various noises and techniques for denoising the color image. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 2(11), pp.217-221.

Boyat, A.K. and Joshi, B.K., 2015. A review paper: noise models in digital image processing. arXiv preprint arXiv:1505.03489. DOI: https://doi.org/10.5121/sipij.2015.6206

Motwani, M.C., Gadiya, M.C., Motwani, R.C. and Harris, F.C., 2004, September. Survey of image denoising techniques. In Proceedings of GSPX (Vol. 27, pp. 27-30). Proceedings of GSPX.

Nachtegael, M., Van der Weken, D., Van De Ville, D. and Kerre, E.E. eds., 2013. Fuzzy filters for image processing (Vol. 122). Springer.

Gonzalez, R.C. and Woods, R.E., 2008. Digital image processing: Pearson International Edition.

Patidar, P., Gupta, M., Srivastava, S. and Nagawat, A.K., 2010. Image de-noising by various filters for different noise. International journal of computer applications, 9(4), pp.45-50. DOI: https://doi.org/10.5120/1370-1846

Ahmad, M.T., Greenspan, M., Asif, M. and Marshall, J.A., 2018, April. Robust Apple Segmentation using Fuzzy Logic. In 2018 5th International Multi-Topic ICT Conference (IMTIC) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/IMTIC.2018.8467275