Efficient Marginal Likelihood Optimization in Blind Deconvolution
In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k|y) and not only its mode. This leads to a distinction between MAPx...
| Main Authors: | , , , |
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| Other Authors: | |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/1721.1/62035 |
| _version_ | 1811091806483382272 |
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| author | Levin, Anat Weiss, Yair Durand, Fredo Freeman, William T. |
| author2 | Fredo Durand |
| author_facet | Fredo Durand Levin, Anat Weiss, Yair Durand, Fredo Freeman, William T. |
| author_sort | Levin, Anat |
| collection | MIT |
| description | In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k|y) and not only its mode. This leads to a distinction between MAPx,k strategies which estimate the mode pair x, k and often lead to undesired results, and MAPk strategies which select the best k while marginalizing over all possible x images. The MAPk principle is significantly more robust than the MAPx,k one, yet, it involves a challenging marginalization over latent images. As a result, MAPk techniques are considered complicated, and have not been widely exploited. This paper derives a simple approximated MAPk algorithm which involves only a modest modification of common MAPx,k algorithms. We show that MAPk can, in fact, be optimized easily, with no additional computational complexity. |
| first_indexed | 2024-09-23T15:08:20Z |
| id | mit-1721.1/62035 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:08:20Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | mit-1721.1/620352019-04-12T11:56:39Z Efficient Marginal Likelihood Optimization in Blind Deconvolution Levin, Anat Weiss, Yair Durand, Fredo Freeman, William T. Fredo Durand Vision In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k|y) and not only its mode. This leads to a distinction between MAPx,k strategies which estimate the mode pair x, k and often lead to undesired results, and MAPk strategies which select the best k while marginalizing over all possible x images. The MAPk principle is significantly more robust than the MAPx,k one, yet, it involves a challenging marginalization over latent images. As a result, MAPk techniques are considered complicated, and have not been widely exploited. This paper derives a simple approximated MAPk algorithm which involves only a modest modification of common MAPx,k algorithms. We show that MAPk can, in fact, be optimized easily, with no additional computational complexity. 2011-04-04T15:45:25Z 2011-04-04T15:45:25Z 2011-04-04 http://hdl.handle.net/1721.1/62035 MIT-CSAIL-TR-2011-020 12 p. application/pdf |
| spellingShingle | Levin, Anat Weiss, Yair Durand, Fredo Freeman, William T. Efficient Marginal Likelihood Optimization in Blind Deconvolution |
| title | Efficient Marginal Likelihood Optimization in Blind Deconvolution |
| title_full | Efficient Marginal Likelihood Optimization in Blind Deconvolution |
| title_fullStr | Efficient Marginal Likelihood Optimization in Blind Deconvolution |
| title_full_unstemmed | Efficient Marginal Likelihood Optimization in Blind Deconvolution |
| title_short | Efficient Marginal Likelihood Optimization in Blind Deconvolution |
| title_sort | efficient marginal likelihood optimization in blind deconvolution |
| url | http://hdl.handle.net/1721.1/62035 |
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