In this paper

we are aiming at random-valued impulse noise detection in two boundaries

using advanced boundary discriminative noise detection(ABDND)and a global histogram to obtain the noise boundary. Although we get good detection results

the rate of false detection increases for ABDND when the range of the noise boundary increases. A modification of the ABDND(MABDND)is therefore proposed in this paper. It includes two stages. First

it uses the global histogram to obtain the noise boundary identical to the ABDND. Second

it uses the statistic of a part of the histogram to find false detected pixels in the first stage

and marks them as uncorrupt pixels. The merit of MABDND is to use the confirmation technique in the second stage to rectify many false detectied pixels in the first stage to keep a low rate for both miss detection and false detection. Image Lena and peppers are used for simulations

and the experimental results show the performance of MABDND is better than that of ABDND

especially

when the range of random-valued is wide MABDND is more robust.