cbi_toolbox.reconstruct.preprocess

The preprocess module implements functions to preprocess experimental images.

cbi_toolbox.reconstruct.preprocess.erase_corners(image_array, corner_size=300)

Fill the corners of the image with the closest pixel value. This is useful when a diaphragm is visible in the field of view.

Parameters:

• image_array (numpy.ndarray) – The original image.

• corner_size (int, optional) – The size of the corner to fill, by default 300

Returns:

The new image with corners filled.

Return type:

numpy.ndarray

cbi_toolbox.reconstruct.preprocess.remove_background_illumination(image_array, threshold=0.5, hole_size=250, margin_size=100, border_axis=-1)

Removes the background illumination from images using thresholding and morphological filtering.

Parameters:

• image_array (numpy.ndarray) – The images to be processed as a 3D array, the first dimension iterates over the different images.

• threshold (float, optional) – The relative threshold used to detect background, by default 0.5.

• hole_size (int, optional) – Biggest holes removed by filtering, by default 250.

• margin_size (int, optional) – Margin kept around the useful information, by default 100.

• border_axis (int, optional) – Axis used to detect the outer edge of the image, by default -1.

Returns:

The array of images without background illumination.

Return type:

numpy.ndarray

cbi_toolbox.reconstruct.preprocess.transmission_to_absorption(image_array, max_value=4096)

Convert a transmission image into an absorption one. This inverses the black-white contrast.

Parameters:

• image_array (numpy.ndarray) – The absorption image.

• max_value (float, optional) – The max value for scaling the original image, by default 4096.

Returns:

The absorption contrast image.

Return type:

numpy.ndarray