Deep learning based super resolution PhD

High-resolution (HR) images and videos are strongly demanded in many applications, such as satellite imaging, medical diagnostic, forensic imaging and video surveillance systems, not only for offering better visualisation but also for extracting additional details. However, in many cases it is difficult to obtain the demanded HR images/videos due to the high cost and inherent physical constraints of the high precision optics and sensors, or the limitation of bandwidth of data communication. Super-resolution techniques have emerged as an alternative low-cost solution, which aims to produce a single HR image or a set of HR images from a sequence of observed low-resolution images captured from the same scene.

The student will be based at Through-life Engineering Services (TES) Centre at Cranfield. The TES Centre focuses on developing knowledge, technology and process demonstrators to provide the capability for the concept design of high value engineering systems based on design and manufacturing for through-life engineering services. The student will also work with the core industrial partners of TES Centre to develop a user case to apply this technique to digitalize industrial components.