The color image has different edge profiles depending on the local characteristics of the images. This paper discusses an adaptive image sharpening method depending on edge slopes of color images. First, the edge strengths of color image are measured by Gaussian derivative spatial filter for detecting the differential edge slopes. Secondly, the segmented mask pattern is generated to characterize the measured edge strengths, such as hard, medium, soft, and gentle or flat. Finally, the color image is processed by switching the plural number of edge sharpening spatial filters according to the mask pattern. The edge sharpening spatial filters worked well without coloring the gray edges when applied to the luminance signal converted from RGB signals by linear matrix. In the gentle or flat areas, all the edge sharpening filters are suppressed not to enhance the flat area noises. Here the multiple second derivative filters such as Gaussian or Gabor with different σ are applied to restore the edge sharpness depending on the mask patterns. The proposed method resulted in the dramatic improvements in the reduction of flat areas' noise, and the image sharpness is recovered in smooth and in natural adaptive to the edge strengths.