There are several modules that enable direct or indirect editing of the SPM data. In principal, most of the data processing modules change the data in one way or another. However, in this section we would like to describe the modules and tools that are specifically designed to correct local defects in an image. The functions below remove “bad” data from an image, and then fill it in using an interpolation algorithm.
The Remove Spots tool can be used for removing very small parts of the image that are considered a scanning error, dust particle or anything else that should not be present in the data. Note that doing so can dramatically alter the resulting statistical parameters of the surface, so be sure not to remove things that are really present on the surface.
While using this tool you can pick up position of the spot to magnify its neighbourhood in the tool window. Then, in the tool window, select a rectangle around the area that should be removed. You can then select one of several interpolation methods for creating data in place of the former “spot”:
Clickingwill execute the selected algorithm.
This simple tool removes manually selected connected parts of mask or interpolates the data under them, or possibly both. The part of mask to remove is selected by clicking on it with left mouse button.
Scars (or stripes, strokes) are parts of the image that are corrupted by a very common scanning error: local fault of the closed loop. Line defects are usually parallel to the fast scanning axis in the image. This function will automatically find and remove these scars, using neighbourhood lines to “fill-in” the gaps. The method is run with the last settings used in Mark Scars.
Mark Scars module can create a mask
of the points treated as scars. Unlike
which directly interpolates the located defects, this module lets you
interactively set several parameters which can fine-tune the scar
After clickingthe new scar mask will be applied to the image. Other modules or tools can then be run to edit this data.
This function substitutes the data under the mask by the solution of solving the Laplacian equation. The data values around the masked areas define the boundary conditions. The solution is calculated iteratively and it can take some time to converge.
The Fractal Correction module, like the Remove Data Under Mask module, replaces data under the mask. However, it uses a different algorithm to come up with the new data: The fractal dimension of the whole image is first computed, and then the areas under the mask are substituted by a randomly rough surface having the same fractal dimension. The root mean square value of the height irregularities (roughness) is not changed by using this module.
This module creates mask of areas in the data that not pass the 3σ criterion. All the values above and below this confidence interval are marked in mask and can be edited or processed by other modules afterwards.
Profiles taken in the fast scanning axis (usually x-axis) can be mutually shifted by some amount or have slightly different slopes. The basic line correction functions deal with this type of discrepancy. Several functions can be used: The Polynomial and Path level tools and then several procedures under → menu.
The Polynomial tool fits each horizontal or vertical line by a polynomial up to the third order and then subtracts the fitted polynomial – a very frequently used function in basic processing of raw SPM data. It also permits to exclude or include selected area from the fit. The inclusion or exclusion only applies to the lines interseting the selected area. Other lines are always fitted using all data values.
Line correction functions inperform only horizontal line corrections, therefore one has to rotate the image to perform column-wise correction. They include:
The first three are very similar, they all align rows of the data field to minimize some quantity. As the names indicate,matches line medians while attempts to match line (pseudo)modus. minimizes certain line difference function that gives more weight to flat areas and less weight to areas with large slopes. The effect of all three functions is often very similar, although some can be more suitable for certain type of data than others.
Functionshifts the lines so that the median of differences (between vertical neighbour pixels) becomes zero, instead of the difference of medians. Therefore it better preserves large features while it is more sensitive to completely bogus lines.
Functiondiffers from the others. It attempts to identify misaligned segments within the rows and correct the height of each such segment individually. Therefore it is often able to correct data with discontinuities in the middle of a row. This function is rather experimental and the exact way it works can be subject of futher changes.
The Path Leveling tool can be used to correct the heights in an arbitrary subset of rows in complicated images.
First, one selects a number of straight lines on the data. The intersections of these lines with the rows then form a set of points in each row that is used for leveling. The rows are moved up or down to minimize the difference between the heights of the points of adjacent rows. Rows that are not intersected by any line are not moved (relatively to neighbouring rows).
Unrotate can automatically make principal directions in an image parallel with horizontal and/or vertical image edges. For that to work, the data need to have some principal directions, therefore it is most useful for scans of artifical and possibly crystallic structures.
The rotation necessary to straighten the image – displayed as Correction – is calculated from peaks in angular slope distribution assuming a prevalent type of structure, or symmetry. The symmetry can be estimated automatically too, but it is possible to select a particular symmetry type manually and let the module calculate only corresponding rotation correction. Note if you assume a structure type that does not match the actual structure, the calculated rotation is rarely meaningful.
It is recommended to level (or facet-level) the data first as overall slope can skew the calculated rotations.
The assumed structure type can be set with Assume selector. Following choices are possible:
Automatically detected symmetry type, displayed above as Detected.
Parallel lines, one prevalent direction.
Triangular symmetry, three prevalent directions (unilateral) by 120 degrees.
Square symmetry, two prevalent directions oriented approximately along image sides.
Rhombic symmetry, two prevalent directions oriented approximately along diagonals. The only difference from Square is the preferred diagonal orientation (as opposed to parallel with sides).
Hexagonal symmetry, three prevalent directions (bilateral) by 120 degrees.