Abstract
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.
Published Version
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