Deep learning based super resolution PhD

This PhD project aims to develop a deep learning based method to improve the resolution of images and videos based on public dataset and experimental dataset.

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.

At a glance

• Application deadlineOngoing
• Award type(s)PhD
• Start dateAs soon as possible
• Duration of award3 years
• EligibilityEU, Rest of World
• Reference numberSATM0002

Entry requirements

Applicants should have a first or second class UK honours degree or equivalent in a related discipline, such as computer science, mathematics, or engineering. The candidate should be self-motivated and have excellent analytical, programming, reporting and communication skills.

Funding
Self-funded
Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network.  This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

For further information please contact:
Dr Yifan Zhao
[email protected]

For further information contact us today:
T: 44 (0)1234 758540
E: [email protected]