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A new grey filter model, named grey stepwise prediction model, is proposed. The new filter model for the image denoising is based on each noisy pixel's neighborhoods stepwise, which is the eight pixels around the noisy pixel, to predict its intensity value and to solve the problems which exist in the image denoising filter method.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The experiment results show that the improved filter model can effectively eliminate image noise, preserve the image's details and edges, increase SNR (signal\u2010to\u2010noise ratio) as well as PSNR (peak signal\u2010to\u2010noise ratio), reduce MSE (mean square error) and MAE (mean absolute error), and significantly improve the image's visual effect.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>The new filter method exposed in the paper can be used to 8\u2010bit gray\u2010scale image denoising. The method can also be used to binary image denoising.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The paper succeeds in constructing a novel filter method for image denoding, and it is undoubtedly a new development in image recovery algorithm and grey systems theory.<\/jats:p><\/jats:sec>","DOI":"10.1108\/20439371211197659","type":"journal-article","created":{"date-parts":[[2012,1,28]],"date-time":"2012-01-28T07:09:04Z","timestamp":1327734544000},"page":"36-44","source":"Crossref","is-referenced-by-count":3,"title":["Stepwise ratio GM (1,1) model for image denoising"],"prefix":"10.1108","volume":"2","author":[{"given":"Jinshuai","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Sujin","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Liu","family":"Xin","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022031820193321200_b1","doi-asserted-by":"crossref","unstructured":"Chow, T.W., Li, X.D. and Cho, S.Y. 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