According to the B-spline convolution mask, first, the contrast sensitiveness （CS） is computed and then is viewed as a noise sensitiveness coeficient （NSC） to adaptively determine a noise-recognized threshold valu...According to the B-spline convolution mask, first, the contrast sensitiveness （CS） is computed and then is viewed as a noise sensitiveness coeficient （NSC） to adaptively determine a noise-recognized threshold value. Based on the noise density function （NDF） in a 3×3 window, the filtering window size is adaptively adjusted, and then a median filter is used to eliminate the noise-marked pixels. The experiment results show that the proposed algorithm can preserve image detail information well and effectively remove the noises, particularly the impulse noises that is also called salt-and-pepper noises superimposed on the computed tomography （CT） and magnetic resonance imaging （MRI） medical images.展开更多
基金supported by Foundation of 11th Five-year Plan for Key Construction Academic Subject (Optics) of Hunan Province,PRC, Outstanding Young Scientific Research Fund of Hunan Provincial Education Department, PRC (No. 09B071)Scientific Research Fund of Hunan Provincial Education Department, PRC(No. 06C581)
文摘According to the B-spline convolution mask, first, the contrast sensitiveness （CS） is computed and then is viewed as a noise sensitiveness coeficient （NSC） to adaptively determine a noise-recognized threshold value. Based on the noise density function （NDF） in a 3×3 window, the filtering window size is adaptively adjusted, and then a median filter is used to eliminate the noise-marked pixels. The experiment results show that the proposed algorithm can preserve image detail information well and effectively remove the noises, particularly the impulse noises that is also called salt-and-pepper noises superimposed on the computed tomography （CT） and magnetic resonance imaging （MRI） medical images.
基金国家自然科学基金(61502137)中央高校基本科研业务费(NJ2019010)+4 种基金南京大学计算机软件新技术国家重点实验室开放课题(KFKT2018B20)香港岭南大学香港商学研究所2019-20种子研究基金(190-009)香港岭南大学种子研究基金(102367)香港岭南大学陈斌博士数据科学机构项目(LEO Dr David P.Chan Institute of DataScience)南京航空航天大学研究生创新基地(实验室)开放基金(Kfjj20191601)。
