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Template matching with noisy patches: A contrast-invariant GLR test

http://www.firstlight.cn2013/4/28

[作者] Charles-Alban Deledalle Loic Denis Florence Tupin

[摘要] Matching patches from a noisy image to atoms in a dictionary of patches is a key ingredient to many techniques in image processing and computer vision. By representing with a single atom all patches that are id…

[关键词] Template matching Likelihood ratio test Detection theory Image restoration

Matching patches from a noisy image to atoms in a dictionary of patches is a key ingredient to many techniques in image processing and computer vision. By representing with a single atom all patches that are identical up to a radiometric transformation, dictionary size can be kept small, thereby retaining good computational efficiency. Identification of the atom in best match with a given noisy patch then requires a contrast-invariant criterion. In the light of detection theory, we propose a new criterion that ensures contrast invariance and robustness to noise. We discuss its theoretical grounding and assess its performance under Gaussian, gamma and Poisson noises.

存档附件原文地址

原文发布时间:2013/3/25

引用本文:

Charles-Alban Deledalle;Loic Denis;Florence Tupin.Template matching with noisy patches: A contrast-invariant GLR testhttp://igsnrr.firstlight.cn/View.aspx?infoid=3197410&cb=Z07870000000
发布时间:2013/3/25.检索时间:2024/12/15

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