An Efficient System to Remove Image Noise by using External and Internal Correlations
Removing noise from the original image is still a challenging problem for researchers. In this paper, we build 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 propose 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.