Speaker
Antonie Brožová
Description
Inverse problems in imaging, like denoising, inpainting or superresolution usually require a suitable regularization of prior to achieve good reconstruction results. It was shown that untrained neural networks can replace traditional handcrafted priors and achieve superior performance. This contribution will focus on Deep Image Prior, the pioneering work utilizing untrained neural priors, its applications for image reconstruction problems, and possible explanations of its success.
