Computational Imaging with Deep Learning

Tuesday, October 15, 2019 - 3:25pm - 4:10pm
Keller 3-180
Orazio Gallo (NVIDIA Corporation)
Neural networks have surpassed the performance of virtually any traditional computer vision algorithm thanks to their ability to learn priors directly from the data. The common and relatively simple encoder/decoder architecture, for instance, has pushed the state-of-the-art of a number of tasks, from optical flow estimation, to image deblurring, image denoising, and even higher level tasks, such as image-to-image translation. To improve the results further, one must leverage the constraints of the specific problem at hand.
In this talk I will use a few of my recent works to show an example of how traditional computational imaging concepts can be combined with deep learning architectures to advance the state-of-the-art.