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2006
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Robust Kernel Regression for Restoration and Reconstruction of Images from Sparse Noisy Data

12 years 3 months ago
Robust Kernel Regression for Restoration and Reconstruction of Images from Sparse Noisy Data
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 including efficient image upscaling, high quality reconstruction of an image from as little as 15% of its (irregularly sampled) pixels, super-resolution from noisy and under-determined data sets, state of the art denoising of images corrupted by Gaussian and other noise, effective removal of compression artifacts; and more.
Hiroyuki Takeda, Sina Farsiu, Peyman Milanfar
Added 22 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2006
Where ICIP
Authors Hiroyuki Takeda, Sina Farsiu, Peyman Milanfar
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