Flattening Images with Step Edges and Atomic or Molecular Resolution

This example continues with the Python shell session from the previous topic Flattening Images with Step Edges. In this example you learn how to flatten an image with step edges which does have molecular or atomic resolution. Locating step edges on images with clear atomic contrast is difficult because the step finding routine is looking for high gradients in the image, but each atom has a steep gradient surrounding it. For flattening the following functions are suitable:

For locating regions in an image:

For locating step edges in an image:

Tutorial Example

Open the demo image image_with_step_edge_and_atoms.dat:

>>> im = spiepy.demo.load_demo_file(demos[2])
_images/image_with_step_edge2_original_image.png

The variable im is the original demo image. Before trying to locate the step edge, remove the tilt on the surface as much as possible. This improves the contrast of the step. Only first order polynomial planes should be used as higher order polynomials will try to fit the step. Either the function flatten_poly_xy() or flatten_xy() can be used to do this. Now flatten the image with the function flatten_xy():

>>> im_flat, _ = spiepy.flatten_xy(im)

The variable im_flat contains the flattened image, it is the input image minus fitted plane. im is not overwritten, the data will be flattened again later and it is best to avoid cumulative flattening where possible. The flattened image looks like:

_images/image_with_step_edge2_preflattened_image.png

While the contrast of the step edge is quite strong it is clear that there is a strong change in contrast between each atom and the space around it. The contrast of the atoms must be removed. The function locate_regions() will locate the positions of the atoms and will create an image from the input image with adjusted contrast. Make it so:

>>> regions, peaks = spiepy.locate_regions(im_flat)

Showing now the flattened images with atoms marked and with adjusted contrast.

_images/image_with_step_edge2_preflattened_image_with_marks.png

_images/image_with_step_edge2_peak_regions.png

The contrast of the step is now very clear in the image. Find the step edge in regions with the function locate_steps():

>>> mask = spiepy.locate_steps(regions, 5)

Showing now the flattened image with the mask (step edge).

_images/image_with_step_edge2_preflattened_image_with_mask.png

Once the step edges has been successfully located, the image can be re-flattened with the function flatten_xy(). Flatten the original image again using the keyword argument mask and the variable mask:

>>> im_final, _ = spiepy.flatten_xy(im, mask)
_images/image_with_step_edge2_flattened_image.png

It is clear that each terrace is now free of its gradient. A final check which can be applied is to plot a histogram of the pixel heights for the images im_flat and im_final.

_images/image_with_step_edge2_histogram.png

The two terraces are clearly separated!