Events

In synchrotron tomography, experimental constraints can impose dose and time constraints that leads to noise that carries over into the reconstructed images. Convolutional neural networks (CNNs) have rapidly gained popularity as a powerful tool for removing noise from reconstructed images. However, training CNNs typically requires collecting a dataset of paired noisy and high-quality measurements, which is a major obstacle to their use in practice. To circumvent this problem, we have proposed a method for training deep convolutional neural networks to denoise tomographic reconstructions that does not require any noise-free data. We present results on challenging dynamic micro-tomography and X-ray diffraction computed tomography datasets. In addition, we discuss challenges and provide an outlook on future work.

Bio:
Allard A. Hendriksen received the M.Sc. degree in mathematics from the University of Leiden, Leiden, The Netherlands, in 2017. He is currently pursuing a Ph.D. degree with the Computational Imaging group at CWI, the national research institute for mathematics and computer science in Amsterdam, The Netherlands. The focus of his research is on combining deep learning and tomographic reconstruction algorithms.