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.