12 March 2015 Parameterized modeling and estimation of spatially varying optical blur
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Optical blur can display significant spatial variation across the image plane, even for constant camera settings and object depth. Existing solutions to represent this spatially varying blur requires a dense sampling of blur kernels across the image, where each kernel is defined independent of the neighboring kernels. This approach requires a large amount of data collection, and the estimation of the kernels is not as robust as if it were possible to incorporate knowledge of the relationship between adjacent kernels. A novel parameterized model is presented which relates the blur kernels at different locations across the image plane. The model is motivated by well-established optical models, including the Seidel aberration model. It is demonstrated that the proposed model can unify a set of hundreds of blur kernel observations across the image plane under a single 10-parameter model, and the accuracy of the model is demonstrated with simulations and measurement data collected by two separate research groups.
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Jonathan D. Simpkins, Jonathan D. Simpkins, Robert L. Stevenson, Robert L. Stevenson, "Parameterized modeling and estimation of spatially varying optical blur", Proc. SPIE 9404, Digital Photography XI, 940409 (12 March 2015); doi: 10.1117/12.2084592; https://doi.org/10.1117/12.2084592


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