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Comparative Analysis of Image Denoising Using Different Filters

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International Conference on Innovative Computing and Communications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 471))

Abstract

The quality of images is often hampered due to the presence of noise. There are different image denoising techniques that can be used. One such technique is the use of filters. Filters are used for enhancing the appearance of images by eliminating unwanted information. We provide a detailed comparative analysis of different filters that can be used in denoising images containing various noises, in this paper. Four different noises, Speckle, Salt and Pepper, Gaussian, and Poisson, have been considered and different filters like Bilateral, Wiener, Mean, and Median have been applied to images containing each of them. The different filtered output images have been compared with the original image using their structural similarity index. Through observation and experimentation, new combinations of filters like Multiple Mean and Median-Mean have been introduced. The processing time has been calculated to decide upon the performance of different filters. A conclusion has been drawn as to which filter has to be used for denoising images containing different noises.

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References

  1. Alisha PB, Sheela KG (2016) Image denoising techniques-an overview. IOSR J Electron Commun Eng 11(1):78–84

    Google Scholar 

  2. Khireddine A, Benmahammed K, Puech W (2007) Digital image restoration by Wiener filter in 2D case. Adv Eng Softw 38(7):513–516

    Google Scholar 

  3. Zhang M, Gunturk BK (2008) Multiresolution Bilateral filtering for image denoising. IEEE Trans Image Process 17(12):2324–2333

    Google Scholar 

  4. Zhang P, Li F (2014) A new adaptive weighted mean filter for removing salt-and-pepper noise. IEEE Signal Process Lett 21(10):1280–1283

    Google Scholar 

  5. Chang C-C, Hsiao J-Y, Hsieh C-P (2008) An adaptive median filter for image denoising. In: Second international symposium on intelligent information technology application, pp 346–350

    Google Scholar 

  6. Sachin Kumar S, Mohan N, Prabaharan P, Soman KP (2016) Total variation denoising based approach for R-peak detection in ECG signals. Procedia Comput Sci 93:697–705

    Google Scholar 

  7. Selvin S, Ajay SG, Gowri BG, Sowmya V, Soman KP (2016) l1 Trend filter for image denoising. In: 6th International conference on advances in computing & communications, vol 93, pp 495–502

    Google Scholar 

  8. Manju BR, Sneha MR (2020) ECG denoising using wiener filter and Kalman filter. Third Int Conf Comput Netw Commun 171:273–281

    Google Scholar 

  9. Jacob NV, Sowmya V, Soman KP (2018) A comparative analysis of total variation and least square based hyperspectral image denoising methods. In: International conference on communication and signal processing, pp 58–63

    Google Scholar 

  10. Srikanth M, Gokul Krishnan KS, Sowmya V, Soman KP (2017) Image denoising based on weighted regularized least square method. In: 2017 International conference on circuits power and computing technologies, pp 1–5

    Google Scholar 

  11. Laxman L, Kamalaveni V, Narayanankutty KA (2013) Comparative study on image restoration techniques using the partial differential equation and filters. Int J Eng Res Technol 2(7):55–59

    Google Scholar 

  12. Dixon KDM, Ajay A, Sowmya V, Soman KP (2016) Aerial and satellite image denoising using least square weighted regularization method. Indian J Sci Technol 9(30):1–10

    Google Scholar 

  13. Elhoseny M, Shankar K (2019) Optimal bilateral filter and Convolutional Neural Network based denoising method of medical image measurements. Elsevier Meas 143:125–135

    Article  Google Scholar 

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Correspondence to Duvvuri Kavya .

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Kavya, D., Jaswanth, K., Chethana, S., Shruti, P., Sarada, J. (2023). Comparative Analysis of Image Denoising Using Different Filters. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 471. Springer, Singapore. https://doi.org/10.1007/978-981-19-2535-1_21

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  • DOI: https://doi.org/10.1007/978-981-19-2535-1_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-2534-4

  • Online ISBN: 978-981-19-2535-1

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