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 encoder/decoder with skip connections architecture, for instance, has been successfully employed in 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 particular when the domain is fairly well understood, such as the case of computational imaging.
In this talk I will describe a few of my recent projects that build on this observation, ranging from reflection removal, to novel view synthesis, and deblurring.
Orazio Gallo is a Senior Research Scientist at NVIDIA Research. He is interested in computational imaging, computer vision, deep learning and, in particular, in the intersection of the three. Alongside topics such as view synthesis and 3D vision, his recent interests also include integrating traditional computer vision and computational imaging knowledge into deep learning architectures. Previously, Orazio's research focus revolved around tinkering with the way pictures are captured, processed, and consumed by the photographer or the viewer.
Orazio is an associate editor of the IEEE Transactions of Computational Imaging and was an associate editor of Signal Processing: Image Communication from 2015 to 2017. Since 2015 he is also a member of the IEEE Computational Imaging Technical Committee.