New image-processing methods were applied to atomic force microscopy images in order to visualize small details on the surface of virus particles and living cells. Polynomial line flattening and plane fitting of topographical images were performed as first step of the image processing. In a second step, a sliding window approach was used for low-pass filtering and data smoothing. The size of the filtering window was adjusted to the size of the small details of interest. Subtraction of the smoothed data from the original data resulted in images with enhanced contrast. Topographical features which are usually not visible can be easily discerned in the processed images. The method developed in this study rendered possible the detection of small patterns on viral particles as well as thin cytoskeleton fibers of living cells. It is shown that the sliding window approach gives better results than Fourier-filtering. Our method can be generally applied to increase the contrast of topographical images, especially when small features are to be highlighted on relatively high objects.