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Deep Learning based Super Resolution of images and videos based on public dataset and experimental dataset

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Start Date: Negotiable
Eligibility: UK, EU, International
Duration of award: 3 years

Supervisors: Dr Yifan Zhao

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.

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.

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.

Details of partner can be found in:

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.

How to apply:
For further information please contact: Dr Yifan Zhao,
If you are eligible to apply for this research studentship, please complete the online application form.

For further information contact us today:
School of Aerospace, Transport and Manufacturing
T: 44 (0)1234 758008

Funding Notes


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