Abstract
This paper presents a three-stage weighted-mean (TSWM) filter to restore digital images corrupted by high noise density impulsive noise. The proposed filter evaluates the weighted mean of non-noisy pixels (NFP) in \(3\times 3\) or \(5\times 5\) grid where the respective weights taken are proportional to their euclidean distance from the center pixel. The first step functions as a pre-processing step where the noisy pixels with the highest information or least uncertainty are denoised first using the two adjacent pixels in a straight line. And in the third stage, edge cases such as corners and boundaries are handled. The performance comparison of this algorithm is done using a \(512\times 512\) grey-scale Lena image. The experiment results show on an average increment of 2.54 dB and 0.15 dB of Peak signal to noise ratio for high (10% to 90%) and extremely high (90% to 98%) noise densities over popular denoising filters.
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Soni, M., Arya, K.V., Garg, B., Petrlik, I. (2023). Three Stage Weighted-Mean Filter Using Euclidean Distance. In: Arya, K.V., Tripathi, V.K., Rodriguez, C., Yusuf, E. (eds) Proceedings of 7th ASRES International Conference on Intelligent Technologies. ICIT 2022. Lecture Notes in Networks and Systems, vol 685. Springer, Singapore. https://doi.org/10.1007/978-981-99-1912-3_24
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DOI: https://doi.org/10.1007/978-981-99-1912-3_24
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