文件名称:Image-Denoising-by-Adaptive-Kernel-Regression
- 所属分类：
- 行业发展研究
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- [PDF]
- 上传时间：
- 2014-01-22
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- 837kb
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- ionut*****
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This paper introduces an extremely robust adaptive
denoising filter in the spatial domain. The filter is based on
non-parametric statistical estimation methods, and in particular
generalizes an adaptive method proposed earlier by Fukunaga
[1]. To denoise a pixel, the proposed filter computes a locally
adaptive set of weights and window sizes, which can be proven
to be optimal in the context of non-parametric estimation using
kernels. While we do not report analytical results on the statistical
efficiency of the proposed method in this paper, we will discuss
its derivation, and experimentally demonstrate its effectiveness
against competing techniques at low SNR and on real noisy data.
denoising filter in the spatial domain. The filter is based on
non-parametric statistical estimation methods, and in particular
generalizes an adaptive method proposed earlier by Fukunaga
[1]. To denoise a pixel, the proposed filter computes a locally
adaptive set of weights and window sizes, which can be proven
to be optimal in the context of non-parametric estimation using
kernels. While we do not report analytical results on the statistical
efficiency of the proposed method in this paper, we will discuss
its derivation, and experimentally demonstrate its effectiveness
against competing techniques at low SNR and on real noisy data.
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Image Denoising by Adaptive Kernel Regression.pdf