Implementation of Novel Image Denoising System by Exploring Both Internal and External Correlations
Removing noise from the original image is still a challenging problem for researchers. In this paper, we
implement the internal and external data cubes to finding the similar patches from the noisy and web images respectively.
We proposed two stages using different filtering approaches for reducing noise. In the first step, the noisy patch may create
incorrect 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. By combining the internal and external
denoising patches, we obtain a preliminary denoising result. In the second step, On transform domain, we propose to reduce
noise by filtering of external and internal cubes, respectively. In this stage, the preliminary denoising result not only
enhances the patch matching accuracy but also provides reliable estimates of filtering parameters.
In this paper we Implement system with the enhancement of previous system for image denoising by exploring both internal
and external correlations. Correlations and a graph optimization method to improve patch matching accuracy and introduce a
more effective filtering methods.
Keywords - Image denoising, external correlations, internal correlations, web images.