Invited talk: Self-supervised deep denoising for synchrotron tomography
Abstract
In synchrotron tomography, experimental constraints can impose dose and time constraints that lead 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.
I was invited to give a talk in the webinar on AI applied to X-ray and synchrotron techniques organized by the ESRF. The recording can be found on YouTube.
