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hello friends in this video we start with the in density transformations in spatial domain let us first consider the image negative transformation for converting the black regions to white and vice-versa for an input image R and an output image s the transformation is represented by s is equal to L minus 1 minus R or s is equal to 255 minus R where L minus 1 represents the maximum intensity which is usually 255 image negative is particularly suited for enhancing white or gray level details embedded in dark regions of an image especially when the black areas are dominant in size let us now try to compute image negative using Python so as we have said earlier we start with import CV to library then read the image so the image Im going to use here is a digital mammogram this is the image so this digital mammogram image has a small lesion it is the same example used in the book digital image processing which is given in the references of this video so we are going to read this image so I