2014 Poster Sessions : Automating the Design of Image Processing Pipelines for Novel Color Filter Arrays: Local, Linear, Learned (L3) Method

Student Name : Qiyuan Tian
Advisor : Brian Wandell
Research Areas: Information Systems
The high density of pixels in modern color sensors provides an opportunity to experiment with new color filter array (CFA) designs. A significant bottleneck in evaluating new designs is the need to create demosaicking, denoising and color transform algorithms tuned for the CFA. To address this issue, we developed a method (local, linear, learned or L3) for automatically creating an image processing pipeline. In this work we describe the L3 algorithm and illustrate how we created a pipeline for a CFA organized as a 2×2 RGB/W block containing a clear (W) pixel. Under low light conditions, the L3 pipeline developed for the RGB/W CFA produces images that are superior to those from a matched Bayer RGB sensor. We also use L3 to learn pipelines for other RGB/W CFAs with different spatial layouts. The L3 algorithm shortens the development time for producing a high quality image pipeline for novel CFA designs.

Qiyuan Tian is a Ph.D. candidate in the Department of Electrical Engineering at Stanford University. He received M. Sc. degree in Electrical Engineering from Stanford University in 2013, and B.Eng. degree in Communication Science and Engineering from Fudan University, China, in 2011. His research interests include magnetic resonance imaging, neuroimaging and digital imaging.