At a Glance
- Tasks: Research dynamic range compression in human and machine vision using cutting-edge ML techniques.
- Company: Join the Colour & Imaging Lab at the University of East Anglia.
- Benefits: Fully funded studentship covering tuition fees and a stipend for 3 years.
- Other info: Exciting opportunity for career growth in a collaborative academic environment.
- Why this job: Contribute to groundbreaking research that bridges human vision and machine learning.
- Qualifications: 2:1 degree in Mathematics, Computer Science, Physics or related fields required.
The predicted salary is between 21805 - 21805 £ per year.
We live in an era where machine learning (ML) based systems can solve a wide variety of image-based problems. However, the architectures deployed are not biologically plausible and the training required to use them can be orders of magnitude more complex than that available to human vision. Further, there are still visual competences where we as human observers outperform even the most complex networks. One such competence is dynamic range compression (related to tone mapping) where an initial image has its dynamic range reduced.
In dynamic range compression (DRC), a large 10,000:1 signal range e.g. images of scenes where there is strong sunlight and deep shadows - is compressed by two orders of magnitude to 100:1. The human vision systems and ML-based algorithms attempt to compress the dynamic range so that the bit-depth compressed image looks good. DRC is a key component in all photographic processing pipelines.
In this project, we will investigate the similarities and divergences between architectures for solving the dynamic range compression in human and machines. Our aim is to design an architecture that is consistent with human vision but where modern machine learning techniques might be deployed to optimise performance. Our work will build on Land and McCann's seminal Retinex theory and its interpretation in the context of known brain physiology, as well as its modern re-engineering as an effective numerical algorithm. We will also investigate why the current non-human-vision-referenced ML algorithms produce poor results.
The research will take place in the Colour & Imaging Lab at the University of East Anglia.
Entry Requirements: The standard minimum entry requirement is 2:1 in Mathematics, Computer Science, Physics and other numerate disciplines.
Mode of Study: Full-time
Start Date: 1 October 2027
Additional Funding Information: This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises home tuition fees and an annual stipend for 3 years. The current UEA / institutional PhD stipend rate is 21,805 and will be subject to any agreed inflationary uplift for 2027/28.
PhD Studentship: Human Referenced Colour Processing in Norwich employer: University of East Anglia
The University of East Anglia (UEA) is an exceptional employer, offering a supportive work culture that prioritises employee growth and development. As a Commercialisation Administrator, you will benefit from a generous benefits package, including 34 days of annual leave, and the opportunity to contribute to meaningful projects that impact society and the economy. UEA's commitment to gender equality, recognised by its Athena Swan Silver Institutional Award, further enhances its reputation as a progressive workplace.
