With the rapid advancement of today’s information technology, the analysis of digital images has endured rapid evolution in technology and is frequently utilized in everyday situations and enterprises. There are specific problems in the transmission process. Images are influenced by the compression of data and communication. In color image processing, a variety of well-established approaches to suppressing noise are offered. The variety of noise corrupting the image determines the nature of the reduction in noise challenge. There have been various linear and nonlinear filtering techniques proposed in the subject of noisy visuals reduction. Noise caused by impulses cannot be effectively eliminated by linear filters because of their propensity to blur an image’s edges. Nonlinear filters, on the other hand, perform well with impulse noise. In recent years, several nonlinear filters based on fuzzy and conventional approaches have been invented. In this paper the denoise adaptive median filter (DAMF) is offered for reducing impulse noise in this study. It includes two stages for filter design. The initial step is the recognition of damaged pixels, then restoring. A fuzzy inference rule membership functions for detection and a traditional median filter for reconstruction.

Keywords:Image denoising, filters, denoise adaptive median filter (DAMF), nonlinear filter, digital image, image processing, Gaussian noise

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1. Hussain A, Javaid Q, Siddique M. Impulse noise removal using fuzzy logic and alpha-trimmed mean. In: 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). 2011 Nov 16. pp. 216–220. IEEE.
2. Alizoti A. Computer – Aided RGB analyses of images. Technium: Romanian J Appl Sci Tech. 2021;3(8):1–12.
3. Taguchi A, Kimura T, Tsuji H. Image enhancement of noisy images by using fuzzy data-dependent bilateral filter. In: 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. 2009 Aug 14. pp. 277–281. IEEE.
4. Mohapatra BN, Panda PP. Histogram equalization and noise removal process for enhancement of image. ACCENTS Transac Image Process Comp Vision. 2017;3(9):22.
5. Chen PY, Lien CY. An efficient edge-preserving algorithm for removal of salt and pepper noise. IEEE Sign Process Lett. 2008;15:833–6.
6. Chen T, Ma KK, Chen LH. Tri-state median filter for image denoising. IEEE Transac Image process. 1999;8(12):1834–8.
7. Chen X, Chen Y. An improved edge detection in noisy image using fuzzy enhancement. In: 2010 International Conference on Biomedical Engineering and Computer Science. 2010 Apr 23. pp. 1–4. IEEE.
8. Van De Ville D, Nachtegael M, Van der Weken D, Kerre EE, Philips W, Lemahieu I. Noise reduction by fuzzy image filtering. IEEE Transac Fuzzy Syst. 2003;11(4):429–36.
9. Russo F, Ramponi G. A fuzzy operator for the enhancement of blurred and noisy images. IEEE Transac Imag Process. 1995;4(8):1169–74.
10. Russo F, Ramponi G. Removal of impulse noise using a FIRE filter. In: Proceedings of 3rd IEEE International Conference on Image Processing. 1996 Sep 19. pp. 975–978. IEEE.
11. Hardie RC, Boncelet C. LUM filters: a class of rank-order-based filters for smoothing and sharpening. IEEE Transac Signal Process. 1993;41(3):1061–76.
12. Kam HS, Tan WH. Impulse detection adaptive fuzzy (IDAF) filter. In: 2009 International Conference on Computer Technology and Development. 2009 Nov 13. pp. 355–359. IEEE.

With the rapid advancement of today’s information technology, the analysis of digital images has endured rapid evolution in technology and is frequently utilized in everyday situations and enterprises. There are specific problems in the transmission process. Images are influenced by the compression of data and communication. In color image processing, a variety of well-established approaches to suppressing noise are offered. The variety of noise corrupting the image determines the nature of the reduction in noise challenge. There have been various linear and nonlinear filtering techniques proposed in the subject of noisy visuals reduction. Noise caused by impulses cannot be effectively eliminated by linear filters because of their propensity to blur an image’s edges. Nonlinear filters, on the other hand, perform well with impulse noise. In recent years, several nonlinear filters based on fuzzy and conventional approaches have been invented. In this paper the denoise adaptive median filter (DAMF) is offered for reducing impulse noise in this study. It includes two stages for filter design. The initial step is the recognition of damaged pixels, then restoring. A fuzzy inference rule membership functions for detection and a traditional median filter for reconstruction.

Keywords:Image denoising, filters, denoise adaptive median filter (DAMF), nonlinear filter, digital image, image processing, Gaussian noise

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Hussain A, Javaid Q, Siddique M. Impulse noise removal using fuzzy logic and alpha-trimmed mean. In: 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). 2011 Nov 16. pp. 216–220. IEEE.
2. Alizoti A. Computer – Aided RGB analyses of images. Technium: Romanian J Appl Sci Tech. 2021;3(8):1–12.
3. Taguchi A, Kimura T, Tsuji H. Image enhancement of noisy images by using fuzzy data-dependent bilateral filter. In: 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. 2009 Aug 14. pp. 277–281. IEEE.
4. Mohapatra BN, Panda PP. Histogram equalization and noise removal process for enhancement of image. ACCENTS Transac Image Process Comp Vision. 2017;3(9):22.
5. Chen PY, Lien CY. An efficient edge-preserving algorithm for removal of salt and pepper noise. IEEE Sign Process Lett. 2008;15:833–6.
6. Chen T, Ma KK, Chen LH. Tri-state median filter for image denoising. IEEE Transac Image process. 1999;8(12):1834–8.
7. Chen X, Chen Y. An improved edge detection in noisy image using fuzzy enhancement. In: 2010 International Conference on Biomedical Engineering and Computer Science. 2010 Apr 23. pp. 1–4. IEEE.
8. Van De Ville D, Nachtegael M, Van der Weken D, Kerre EE, Philips W, Lemahieu I. Noise reduction by fuzzy image filtering. IEEE Transac Fuzzy Syst. 2003;11(4):429–36.
9. Russo F, Ramponi G. A fuzzy operator for the enhancement of blurred and noisy images. IEEE Transac Imag Process. 1995;4(8):1169–74.
10. Russo F, Ramponi G. Removal of impulse noise using a FIRE filter. In: Proceedings of 3rd IEEE International Conference on Image Processing. 1996 Sep 19. pp. 975–978. IEEE.
11. Hardie RC, Boncelet C. LUM filters: a class of rank-order-based filters for smoothing and sharpening. IEEE Transac Signal Process. 1993;41(3):1061–76.
12. Kam HS, Tan WH. Impulse detection adaptive fuzzy (IDAF) filter. In: 2009 International Conference on Computer Technology and Development. 2009 Nov 13. pp. 355–359. IEEE.