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Technical Program
Paper Detail
| Paper: | TA-L6.1 |
| Session: | Signal/Image Reconstruction from Sparse Measurements |
| Time: | Tuesday, October 10, 09:40 - 10:00 |
| Presentation: |
Special Session Lecture
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| Title: |
ROBUST KERNEL REGRESSION FOR RESTORATION AND RECONSTRUCTION OF IMAGES FROM SPARSE NOISY DATA |
| Authors: |
Hiroyuki Takeda; University of California, Santa Cruz | | | | | Sina Farsiu; University of California, Santa Cruz | | | | | Peyman Milanfar; University of California, Santa Cruz | | |
| Abstract: |
We introduce a class of robust non-parametric estimation methods which are ideally suited for the reconstruction of signals and images from noise-corrupted or sparsely collected samples. The filters derived from this class are locally adapted kernels which take into account both the local density of the available samples, and the actual values of these samples. As such, they are automatically steered and adapted to both the given sampling “geometry”, and the samples’ “radiometry”. As the framework we proposed does not rely upon specific assumptions about noise or sampling distributions, it is applicable to a wide class of problems. |
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