Estimating an images blur kernel using natural image statistics, and deblurring it : An analysis of the Goldstein-Fattal method
Despite the significant improvement in image quality resulting from improvement in optical sensors and general electronics, camera shake blur significantly undermines the quality of hand-held photographs. In this work, we present a detailed description and implementation of the blur kernel estimatio...
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| Other Authors: | , |
| Format: | article |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.12008/43540 https://doi.org/10.5201/ipol.2018.211 |
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| Summary: | Despite the significant improvement in image quality resulting from improvement in optical sensors and general electronics, camera shake blur significantly undermines the quality of hand-held photographs. In this work, we present a detailed description and implementation of the blur kernel estimation algorithm introduced by Goldstein and Fattal in 2012. Unlike most methods that attempt to solve an inverse problem through a variational formulation (e.g. through a Maximum A Posteriori estimation), this method directly estimates the blur kernel by modeling statistical irregularities in the power spectrum of blurred natural images. The adopted mathematical model extends the well-known power-law by contemplating the presence of dominant strong edges in particular directions. The blur kernel is retrieved from an estimation of its power spectrum, by solving a phase retrieval problem using additional constraints associated with the particular nature of camera shake blur kernels (e.g. non-negativity and small spatial support). Although the algorithm is conceptually simple, its numerical implementation presents several challenges. This work contributes to a detailed anatomy of the Goldstein and Fattal method, its algorithmic description, and its parameters. |
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