Abstract
Coded exposure photography makes the traditional ill-posed motion deblurring problem well posed. However, how to accurately derive the motion blur length confused many researchers because of the non-smooth blur of the coded exposure image. This chapter proposes a novel approach to automatic estimate the motion blur length by analyzing the image statistics for coded exposure photography. The researchers analyze the image power spectrum statistics and observe that the motion blur length has some relations with the residual sums of squares (RSS) of the image power spectrum statistics in a least squares sense. That is, the power spectrum statistics of the obtained deblurred image using the correct estimated blur length corresponds to the lowest value of the RSS. Given an initial blur length, and using the high-speed direct deconvolution approach, researchers can easily find the correct blur length using a global search method. The experimental results demonstrate the validity of the proposed method.
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Huang, K., Liang, H., Ren, W., Zhang, J. (2013). Motion Blur Identification Using Image Statistics for Coded Exposure Photography . In: Wong, W.E., Ma, T. (eds) Emerging Technologies for Information Systems, Computing, and Management. Lecture Notes in Electrical Engineering, vol 236. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7010-6_52
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DOI: https://doi.org/10.1007/978-1-4614-7010-6_52
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