Joint deblurring and demosaicking of CFA image data with motion blur

Ruiwen Zhen; Robert L. Stevenson

doi:10.1117/12.2040663

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17 February 2014 Joint deblurring and demosaicking of CFA image data with motion blur

Ruiwen Zhen, Robert L. Stevenson

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Proceedings Volume 9029, Visual Information Processing and Communication V; 90290B (2014) https://doi.org/10.1117/12.2040663
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

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.

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Ruiwen Zhen and Robert L. Stevenson "Joint deblurring and demosaicking of CFA image data with motion blur", Proc. SPIE 9029, Visual Information Processing and Communication V, 90290B (17 February 2014); https://doi.org/10.1117/12.2040663

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