Scaling up deep learning-based superresolution models more efficiently using the cloud

Description

Superresolution is a process for obtaining one or more high-resolution images from one or more low-resolution observations. It has been used for many applications, including satellite and aerial imaging, medical image processing, ultrasound imaging, line fitting, automated mosaicking, infrared imaging, facial image improvement, text image improvement, compressed image and video enhancement, and fingerprint image enhancement. While research on superresolution began in the 1970s, recently, with the power of deep learning, many notable new methods have been created, including SRCNN, SRResNet, and lately, SRGANs, which use generative adversarial networks. However, since these approaches require a lot of images to train the deep learning network, they are supercompute intensive. Fortunately, with the power of the cloud, you can easily scale up the compute resources as needed, making the algorithm converge faster.

Xiaoyong Zhu shares the latest academic progress in superresolution using deep learning and explains how it can be applied in various industries, including healthcare. Along the way, Xiaoyong demonstrates how the training can be done in a distributed fashion in the cloud.