![]() ![]() As a general-purpose language, Python is used in synchrotrons to control device servers, to access raw data of X-ray detectors, to reconstruct tomography volumes from radiographs, and in data processing packages for macromolecular crystallography, azimuthal integration of diffraction data, or fluorescence analysis. The Python language is widely used in the scientific world and in synchrotron facilities. Matlab (TM) and its image processing toolbox are popular in the academic community of computer vision and image processing. ![]() Alternatively, the use of a programming language gives finer control, better reproducibility, and more complex analysis possibilities, provided classical processing algorithms can be called from libraries-thereby limiting the complexity of the programming task and the risk of bugs. ![]() Some synchrotrons have even developed their own tools for volume processing, such as Pore3D at the Elettra facility. Software specialized in analyzing synchrotron data is available as well, such as XRDUA for diffraction images obtained in powder diffraction analysis, or for 3D images, commercial tools such as Avizo 3D software (TM), or ToolIP/MAVIkit are appreciated for an intuitive graphical pipeline and advanced 3D visualization. ImageJ and its distribution Fiji is a popular general-purpose tool for 2D and 3D images, thanks to its intuitive menus and graphical tools, and the wealth of plugins contributed by a vivid community. Several software applications and libraries are available to synchrotron users to process their images. Therefore, image processing tools need to offer at the same time enough flexibility of use, a variety of algorithms, and efficient implementations to allow for fast iterations while adjusting the workflow. Image processing necessarily involves trial and error phases to choose the processing workflow. Often, the sequence of operations needed to produce these data is not known beforehand, or might be altered due to artifacts, or to an unforeseen evolution of the sample. Transforming billions of pixels and voxels to a few meaningful figures represents a tremendous data reduction. However, the time subsequently spent in processing the images has not decreased as much, so that the outcome of a successful synchrotron imaging run often takes weeks or even months to be transformed into scientific results. New modalities such as single-bunch imaging provide a time resolution down to the nanosecond for radiography. The acquisition time of synchrotron tomography images has decreased dramatically over the last decade, from hours to seconds. ![]()
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