The course is focused on digital image processing by mathematical methods, especially geometric transformations, resolution, spectral filtering, colour transformations, and modelling. Practical exercises include an introduction to image processing methods in the Matlab environment with the implementation of large image datasets for nanolithography including the use of supercomputing capabilities (IT4I).
Literature
W. Burger, M. J. Burge, Digital Image Processing: An Algorithmic Introduction (3rd ed.), Springer Nature, 2022.
S. E. Umbaugh, Computer Vision and Image Analysis: Digital Image Processing and Analysis, 4th ed., CRC Press, 2023.
O. Marques, G. B. Borba, Image processing recipes in Matlab, CRC Press, 2024.
Advised literature
F. A. Merchant, K. R. Castleman, Microscope image processing, 2nd ed., Elsevier Academic Press, 2023.
M. McBride, Image processing in Python: Processing raster images with the Pillow library, Learnpub, 2021.
Z. Hu, Y. A. Ushenko, I. V. Soltys, O. V. Dubolazov, M. P. Gorksky, O. V. Olar, L. Y. Trifonyuk, Mueller-matrix tomography of biological tissues and fluids: Digital image processing and analysis techniques, Springer, 2024.