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Proceedings Paper

Joint deblurring and demosaicking of CFA image data with motion blur
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Paper Abstract

Camera motion blur is a common problem in low-light imaging applications. It is diffcult to apply image restoration techniques without an accurate blur kernel. Recently, inertial sensors have been successfully utilized to estimate the blur function. However, the effectiveness of these restoration algorithms has been limited by lack of access to unprocessed raw image data obtained directly from the Bayer image sensor.

In the work, raw CFA image data is acquired in conjunction with 3-axis acceleration data using a custom-built imaging system. The raw image data records the redistribution of light but is effected by camera motion and the rolling shutter mechanism. Through the use of acceleration data, the spread of light to neighboring pixels can be determined. We propose a new approach to jointly perform deblurring and demosaicking of the raw image. This approach adopts edge-preserving sparse prior in a MAP framework. The improvements brought by our algorithm is demonstrated by processing the data collected from the imaging system.

Paper Details

Date Published: 17 February 2014
PDF: 7 pages
Proc. SPIE 9029, Visual Information Processing and Communication V, 90290B (17 February 2014); doi: 10.1117/12.2040663
Show Author Affiliations
Ruiwen Zhen, Univ. of Notre Dame (United States)
Robert L. Stevenson, Univ. of Notre Dame (United States)


Published in SPIE Proceedings Vol. 9029:
Visual Information Processing and Communication V
Amir Said; Onur G. Guleryuz; Robert L. Stevenson, Editor(s)

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