Background correction

The raw EBSD signal can be empirically evaluated as a superposition of a Kikuchi diffraction pattern and a smooth background intensity. For pattern indexing, the latter intensity is undesirable, while for so-called virtual backscatter electron (VBSE) imaging, this intensity can reveal important topographical, compositional or diffraction contrast. This section details methods to enhance the Kikuchi diffraction pattern.

Static background correction

The slowly varying diffuse background in raw patterns can be removed by either subtracting or dividing by a static background via static_background_correction():

>>> s.static_background_correction(operation='subtract', relative=True)

The same pattern as acquired (left) and after static background correction (right).

Here the static background pattern is assumed to be stored as part of the signal metadata, which can be loaded via set_experimental_parameters(). The static background pattern can also be passed to the static_bg parameter. Passing relative=True ensures that relative intensities between patterns are kept when the patterns are scaled after correction to fill the intensity range available for the data type, e.g. [0, 255] for uint8.

Dynamic background correction

Uneven intensity in a static background subtracted pattern can be corrected by subtracting or dividing by a dynamic background obtained by Gaussian blurring. This so-called flat fielding is done with dynamic_background_correction(), with possibilities of setting the operation and standard deviation of the Gaussian kernel, sigma:

>>> s.dynamic_background_correction(operation='subtract', sigma=2)

The same pattern after static correction (left) followed by dynamic background correction (right).

Patterns are rescaled to fill the available data type range.

Adaptive histogram equalization

Enhancing the pattern contrast with adaptive histogram equalization has been found useful when comparing patterns for dictionary indexing [Marquardt2017]. With adaptive_histogram_equalization(), the intensities in the pattern histogram are spread to cover the available range, e.g. [0, 255] for patterns of uint8 data type:

>>> s.adaptive_histogram_equalization(kernel_size=(15, 15))

The same pattern after dynamic correction (left) followed by adaptive histogram equalization (right).

The kernel_size parameter determines the size of the contextual regions. See e.g. Fig. 5 in [Jackson2019], also available via EMsoft’s GitHub repository wiki, for the effect of varying kernel_size.


K. Marquardt, M. De Graef, S. Singh, H. Marquardt, A. Rosenthal, S. Koizuimi, “Quantitative electron backscatter diffraction (EBSD) data analyses using the dictionary indexing (DI) approach: Overcoming indexing difficulties on geological materials,” American Mineralogist 102 (2017), doi:


M. A. Jackson, E. Pascal, M. De Graef, “Dictionary Indexing of Electron Back-Scatter Diffraction Patterns: a Hands-On Tutorial,” Integrating Materials and Manufacturing Innovation 8 (2019), doi:

Rescale intensities

Only changing the data type using change_dtype() does not rescale pattern intensities, leading to patterns not using the full available data type range, e.g. [0, 65535] for uint16:

>>> print(,
uint8 255
>>> s.change_dtype(np.uint16)
>>> print(,
uint16 255
>>> s.plot(vmax=1000)

A pattern, initially with uint8 data type, cast to uint16.

In these cases it is convenient to rescale intensities to a desired data type range, either keeping relative intensities between patterns or not, by using rescale_intensities():

>>> s.rescale_intensities(relative=True)
>>> print(,
uint16 65535
>>> s.plot(vmax=65535)

Same pattern as in the above figure with intensities rescaled to fill the full uint16 data range.