IN THIS PAPER, WE PRESENT A ROBUST IMAGE FILTER THAT PROVIDES PRESERVATION OF FINE DETAILS AND EFFECTIVE SUPRESSION OF INTENSIVE MULTIPLICATIVE NOISE. THE FILTER IS BASED ON THE VASE OF m 8ROBUST MAXIMUM LIKELIHOOD) ESTIMATOES AND R (RANK) ESTIMATORS DERIVE FROM THE STATISTICAL THEORY OF RANK TESTER. AT THE FIRS STAGE, TO PROVIDE SPINKE DETECTOR AND A RE-DESCENDING M-ESTIMATOR COMBINED WITH THE MEDIA ESTIMATOR TO REMOVE OUTLIERS IN THE CENTER OF FILTERING WINDOW. AT THE SECOND STAGE, TO PROVIDE THE MELTIPLICATVE NOISE SUPPRESSION A MODIFIED SIGMA FILTER COMBINED WITH DETAIL PRESERVING SEHEME OF A LEE FILTER, IS USED. VISUAL AND QUANTITATIVE ANALYSIS OF THE SIMULATION RESULTS SHOW THAT THE PROPOSED IMAGE FILTER DEMOSTRATES GOOD PRESERVETION OFIN THIS PAPER, WE PRESENT A ROBUST IMAGE FILTER THAT PROVIDES PRESERVATION OF FINE DETAILS AND EFFECTIVE SUPRESSION OF INTENSIVE MULTIPLICATIVE NOISE. THE FILTER IS BASED ON THE VASE OF m 8ROBUST MAXIMUM LIKELIHOOD) ESTIMATOES AND R (RANK) ESTIMATORS DERIVE FROM THE STATISTICAL THEORY OF RANK TESTER. AT THE FIRS STAGE, TO PROVIDE SPINKE DETECTOR AND A RE-DESCENDING M-ESTIMATOR COMBINED WITH THE MEDIA ESTIMATOR TO REMOVE OUTLIERS IN THE CENTER OF FILTERING WINDOW. AT THE SECOND STAGE, TO PROVIDE THE MELTIPLICATVE NOISE SUPPRESSION A MODIFIED SIGMA FILTER COMBINED WITH DETAIL PRESERVING SEHEME OF A LEE FILTER, IS USED. VISUAL AND QUANTITATIVE ANALYSIS OF THE SIMULATION RESULTS SHOW THAT THE PROPOSED IMAGE FILTER DEMOSTRATES GOOD PRESERVETION OF