Joint burst image denoising and deblurring

Capturing high quality images is a major selling point of modern flagship smartphones. However, due to physical constraints on sensor size, capturing a good image with a mobile phone is challenging especially in low-light conditions. To obtain a bright image, one has to use a long exposure or digitally gain up a short exposure image. The former will result in blurry images due to handshake and scene motion, and the latter will yield noisy images. We will explore methods that combine complimentary information from both types of images captured from two separate cameras synchronously. This is particularly interesting for Samsung since modern smartphones already have multiple rear-facing cameras that can be used for this purpose. In this project, we will build on state-of-the-art deep learning models for denoising and deblurring, and explore how to fuse them to jointly process synchronized short and long exposure images.

Faculty Supervisor:

Marcus Brubaker

Student:

Partner:

Samsung Electronics Canada

Discipline:

Computer science

Sector:

Technology; Information and Communications Technology; New and Digital Media

University:

University of Toronto

Program: