Abstract
Image noise can be viewed as unwanted disturbances in a digital image that should be removed or reduced before further processing and analysis. Impulsive noise, also known as impulse noise, is a very disruptive type of noise, characterized by abrupt variations in brightness in a subset of the image pixels. Impulsive noise commonly occurs during image acquisition and transmission. To mitigate its effects, various impulsive noise reduction methods have been proposed by the image processing community. In contrast to classical filters such as the median filter, most current impulsive noise reduction techniques implement a two-step approach that consists of a noise detection phase to identify noisy pixels and a filtering phase to reduce the amount of noise in the presumably corrupted pixels.
The approach presented in this paper is also along this line. To be more precise, we draw on the principles of two state-of-the-art impulsive noise reduction methods, namely the adaptive fuzzy transform based image filter (ATIF) and the improved fuzzy mathematical morphology open-close filter (i-FMMOCS), in order to propose a new method for general impulsive noise reduction.
Supported in part by CNPq under grant no. 315638/2020-6 (“Lattice Computing with an Emphasis on \(\mathbb {L}\)-Fuzzy Systems and Mathematical Morphology”) and FAPESP under grant no. 2020/09838-0 (Brazilian Institute of Data Science) as well as the Grant PID2020-113870GB-I00-“Desarrollo de herramientas de Soft Computing para la Ayuda al Diagnóstico Clínico y a la Gestión de Emergencias (HESOCODICE)”, funded by MCIN/AEI/10.13039/501100011033/.
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References
Kerre, E.E., Nachtegael, M.: Fuzzy Techniques in Image Processing. STUDFUZZ, vol. 52. Springer, New York (2000). https://doi.org/10.1007/978-3-7908-1847-5
Schulte, S., Nachtegael, M., De Witte, V., Van der Weken, D., Kerre, E.E.: A fuzzy impulse noise detection and reduction method. IEEE Trans. Image Process. 15(5), 1153–1162 (2006)
Schuster, T., Sussner, P.: An adaptive image filter based on the fuzzy transform for impulse noise reduction. Soft. Comput. 21(13), 3659–3672 (2017)
Yüksel, M.E., Bastürk, A.: Application of type 2 fuzzy logic filtering to reduce noise in color images. IEEE Comput. Intell. Mag. 7(3), 25–36 (2012)
Chen, C.L.P., Liu, L., Chen, L., Tang, Y.Y., Zhou, Y.: Weighted couple sparse representation with classified regularization for impulse noise removal. IEEE Trans. Image Process. 24(11), 4014–4026 (2015)
Panetta, K., Bao, L., Agaian, S.: A new unified impulse noise removal algorithm using a new reference sequence-to-sequence similarity detector. IEEE Access 6, 37225–37236 (2018)
Rani, S., Chabbra, Y., Malik, K.: Adaptive window-based filter for high-density impulse noise suppression. Measur. Sens. 24, 100455 (2022)
Gonzalez-Hidalgo, M., Massanet, S., Mir, A., Ruiz-Aguilera, D.: Improving salt and pepper noise removal using a fuzzy mathematical morphology-based filter. Appl. Soft Comput. 63, 167–180 (2018)
Sussner, P., Schuster, T.: Linear versus lattice fuzzy transforms: image algebra representation, observations, and results in image denoising. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2018)
Perfilieva, I.: Fuzzy transforms: theory and applications. Fuzzy Sets Syst. 157, 993–1023 (2006)
Sussner, P.: Lattice fuzzy transforms from the perspective of mathematical morphology. Fuzzy Sets Syst. 288, 115–128 (2016)
Ritter, G.X., Wilson, J.N.: Handbook of Computer Vision Algorithms in Image Algebra, 2nd edn. CRC Press, Boca Raton (2001)
Bloch, I., Maitre, H.: Fuzzy mathematical morphologies: a comparative study. Pattern Recogn. 28(9), 1341–1387 (1995)
De Baets, B.: Fuzzy morphology: a logical approach. In: Ayyub, B.M., Gupta, M.M. (eds.) Uncertainty Analysis in Engineering and Science: Fuzzy Logic. Statistics, and Neural Network Approach, pp. 53–67. Kluwer Academic Publishers, Norwell (1997)
Baczyński, M., Jayaram, B.: Fuzzy Implications. STUDFUZZ, vol. 231. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69082-5
Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Academic Publishers, London (2000)
Bindal, N., Ghumaan, R.S., Sohi, P.J.S., Sharma, N., Joshi, H., Garg, B.: A systematic review of state-of-the-art noise removal techniques in digital images. Multimed. Tools Appl. 81(22), 31529–31552 (2022)
Yin, M., Adam, T., Paramesran, R., Hassan, M.F.: An \(\ell \)0-overlap** group sparse total variation for impulse noise image restoration. Signal Process.: Image Commun. 102, 116620 (2022)
Orazaev, A., Lyakhov, P., Baboshina, V., Kalita, D.: Neural network system for recognizing images affected by random-valued impulse noise. Appl. Sci. 13(3) (2023)
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Sussner, P., González-Hidalgo, M. (2023). FIDOC: A New Combination of Fuzzy Impulse Noise Detection and Open-Close Filtering. In: Massanet, S., Montes, S., Ruiz-Aguilera, D., González-Hidalgo, M. (eds) Fuzzy Logic and Technology, and Aggregation Operators. EUSFLAT AGOP 2023 2023. Lecture Notes in Computer Science, vol 14069. Springer, Cham. https://doi.org/10.1007/978-3-031-39965-7_19
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