文件名称:image-denoising
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Abstract—Single image denoising suffers limited data
collection within a noisy image. In this paper, we propose a
novel image denoising scheme, which explores both internal
and external correlations with the help of web images. For
each noisy patch, we build internal and external data cubes
by finding similar patches the noisy and web images,
respectively. We then propose reducing noise by a two-stage
strategy using different filtering approaches. In the first stage,
since the noisy patch may lead to inaccurate patch selection, we
propose a graph based optimization method to improve patch
matching accuracy in external denoising. The internal denoising
is frequency truncation on internal cubes.-Abstract—Single image denoising suffers limited data
collection within a noisy image. In this paper, we propose a
novel image denoising scheme, which explores both internal
and external correlations with the help of web images. For
each noisy patch, we build internal and external data cubes
by finding similar patches the noisy and web images,
respectively. We then propose reducing noise by a two-stage
strategy using different filtering approaches. In the first stage,
since the noisy patch may lead to inaccurate patch selection, we
propose a graph based optimization method to improve patch
matching accuracy in external denoising. The internal denoising
is frequency truncation on internal cubes.
collection within a noisy image. In this paper, we propose a
novel image denoising scheme, which explores both internal
and external correlations with the help of web images. For
each noisy patch, we build internal and external data cubes
by finding similar patches the noisy and web images,
respectively. We then propose reducing noise by a two-stage
strategy using different filtering approaches. In the first stage,
since the noisy patch may lead to inaccurate patch selection, we
propose a graph based optimization method to improve patch
matching accuracy in external denoising. The internal denoising
is frequency truncation on internal cubes.-Abstract—Single image denoising suffers limited data
collection within a noisy image. In this paper, we propose a
novel image denoising scheme, which explores both internal
and external correlations with the help of web images. For
each noisy patch, we build internal and external data cubes
by finding similar patches the noisy and web images,
respectively. We then propose reducing noise by a two-stage
strategy using different filtering approaches. In the first stage,
since the noisy patch may lead to inaccurate patch selection, we
propose a graph based optimization method to improve patch
matching accuracy in external denoising. The internal denoising
is frequency truncation on internal cubes.
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image denoising.pdf