Deep learning-based illuminant estimation for mobile devices

Humans possess the ability to see objects as having the same color even when viewed under different illuminations. Cameras inherently lack this capability. A process called auto white balance (AWB) has to be applied by the camera to mimic this behavior of the human visual system. AWB is one of the first steps in a series of operations performed on-board the camera as the raw image recorded by the sensor is processed. It plays a crucial role in ensuring that the colors in the final image that is output to the user are correctly represented. In recent years, AI algorithms for AWB have demonstrated superior performance over conventional methods. However, existing AI solutions are too computationally expensive for use on smartphones and mobile cameras. The goal of this project is to devise an AI algorithm that is light-weight and capable of running in real time on-device. This project will help Samsung Electronics Canada develop an improved and more practical auto white balance AI algorithm applicable to modern smartphone camera images.

Faculty Supervisor:

Michael S. Brown

Student:

Abdullah Abuolaim

Partner:

Samsung Electronics Canada

Discipline:

Engineering - computer / electrical

Sector:

Manufacturing

University:

York University