Imaging Software

1. Imaging Software

2. Outline • False color mapping • 3-D Topography • Flattening the image • Scars removing • Profiling • Statistical quantities • Basic Filtering

3. Gwyddion

4. Basic Features File: File loading and saving Edit Data Processing Graph Meta View

5. Saving a File as .jpg • Select the file you want to print • Replace the extension .mi by .jpg • Save as …..xxxx.jpg

6. False Color Mapping Edit>Color Gradient>( select color) – set selected items as default. File>Open (select and open) Color Zones “Grey” Scale

7. Removing Scars Correct lines by matching height median Before correction Correct horizontal scars Requires dedication!

8. Automatically Correct Rotation in Horizontal Plane

9. Gwyddion Flattening Before flattening 1 micron After flattening 0.7 micron

10. More Flattening using Gwyddion Un-flattened data Mean plane subtraction Three points plane

11. Measure Distances

12. Extracting Profiles Select a section (or several sections) and get profiles

13. Statistical Quantities Most of the applications relate to the value of RMS. High RMS numbers tell us that the surface is “rough”

14. User’s GuideStatistical Quantities Tool http://gwyddion.net/documentation/user-guide/statistical-analysis.html • Mean value, minimum, maximum and median. • RMS value of the height irregularities: this quantity is computed from data variance. • Ra value of the height irregularities: this quantity is similar to RMS value with the only difference in exponent (power) within the data variance sum. As for the RMS this exponent is q = 2, the Ra value is computed with exponent q = 1 and absolute values of the data (zero mean). • Height distribution skewness: computed from 3rd central moment of data values. • Height distribution kurtosis: computed from 4th central moment of data values. • Projected surface area and surface area: computed by simple triangulation. • Mean inclination of facets in area: computed by averaging normalized facet direction vectors.

15. Skewness and Kurtosis • Skewness is the lack of symmetry of a distribution. The skewness for a normal distribution is zero • Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution. That is, data sets with high kurtosis tend to have a distinct peak near the mean, decline rather rapidly, and have heavy tails. Data sets with low kurtosis tend to have a flat top near the mean rather than a sharp peak.

16. Crop data

17. Meta

18. 3-D Topography

19. Basic Filters • Mean filter – takes the mean value of neighborhood of the filtered value as the value. • Median filter – takes the median value of neighborhood of the filtered value as the value. • Conservative denoise filter – checks whether the value is not extreme within the neighborhood. If yes, filter substitutes the value by of the next highest (lowest) value. • Kuwahara filter – is an edge-preserving smoothing filter. • Minimum filter – also known as erode filter, replaces values by minimum found in neighborhood. • Maximum filter – also known as dilate filter, replaces values by maximum found in neighborhood. • Dechecker filter – a smoothing filter specially designed to remove checker pattern from the image while preserving other details. It is a convolution filter with kernel

20. Basic Filtering Flattened and scars corrected Raw data .. Plus Medium filtering

21. SPIP

22. Main Features

23. Sub-menu

24. SPIP Noise Removing Horizontal noise present on the top left corner is removed on the right corner figure. The difference does not show any feature and we can consider that the original data are still present in the filtered data. http://www.imagemet.com/index.php?main=products&sub=tutorials