Accelerating a learning-based image processing pipeline for digital cameras

Qiyuan Tian & Haomiao Jiang

(Stanford University)

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Description:

L3 (Local, Linear, Learned) is a new technology to automate and customize the design of image processing pipelines for cameras with novel architecture, such as unconventional color filter arrays. L3 classifies sensor image pixels into categories that are local in space and response and automatically learns linear operators that transform pixels to the calibrated output space using training data from camera simulation. The local and linear processing of individual pixels makes L3 ideal for parallelization. We accelerated the L3 pipeline on NVIDIA® Shield™ Tablets using GPUs for real time rendering of video captured by a multispectral camera prototype. The combination of L3 and GPUs delivers high performance with low power for image processing on mobile devices.

Further Information:

Qiyuan Tian is a Ph.D. Candidate in the Department of Electrical Engineering at Stanford University. He received B.Eng. (2011) in Communication Science and Engineering at Fudan University, China, and M.S. (2013) in Electrical Engineering at Stanford University. He studied as an undergraduate exchange student (2009) in the Department of Electronic and Computer Engineering at The Hong Kong University of Science and Techonology. He is working on digital imaging, magnetic resonance imaging and neuroimaging.

Haomiao Jiang is a Ph.D. candidate in the Department of Electrical Engineering at Stanford University. He received B.A. (2011) in Information Security at Shanghai Jiao Tong University, China, and M.S. (2013) in Electrical Engineering at Stanford University. He is working with Professor Brian Wandell on color vision, display modeling and computational photography.




Created: Friday, April 10th, 2015