Erkan, UğurGökrem, LeventEnginoğlu, Serdar2025-01-212025-01-2120191308-5514https://dergipark.org.tr/tr/download/article-file/766584https://dergipark.org.tr/tr/pub/umagd/issue/43865/495904https://doi.org/10.29137/umagd.495904https://hdl.handle.net/20.500.12587/19259In image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image. The adaptive filters being a kind of the nonlinear filters mainly perform better than the others in salt-and-pepper noise. In this paper, we first define a new median method, i.e. right median(rm). We then define a new adaptive nonlinear filter developed via rm, namely Adaptive Right Median Filter (ARMF), for saltand-pepper noise removal. Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that ARMF outperforms the other methods at all the noise density except 80% and 90% in the mean percentages. Finally, we discuss the need for further research.In image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image. The adaptive filters being a kind of the nonlinear filters mainly perform better than the others in salt-and-pepper noise. In this paper, we first define a new median method, i.e. right median(rm). We then define a new adaptive nonlinear filter developed via rm, namely Adaptive Right Median Filter (ARMF), for saltand-pepper noise removal. Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that ARMF outperforms the other methods at all the noise density except 80% and 90% in the mean percentages. Finally, we discuss the need for further research.eninfo:eu-repo/semantics/openAccessImage denoisingNoise removalNonlinear filtersImage denoisingNoise removalNonlinear filtersNonlinear functionsMatrix algebraEngineeringArticle12-54255010.29137/umagd.495904495904