A new correlation-differential denoising algorithm

We propose a new correlation-differential denoising algorithm based on two novel concepts of correlation-weighted and differential-weighted filters for Gaussian noise. The correlation-weighted filter utilizes the correlation of different sequences in an image in different directions as a weight to perform the filtering. This filter can preserve the texture information, especially the edge information. Derived from the Gaussian filter, the differential-weighted filter uses the actual difference of pixel values as a parameter instead of the distance relationship of pixels' positions. These two filters have the complementary function that preserve the texture information while simultaneously removing the noise. The algorithm is shown to outperform current denoising standards, including Gaussian filtering, non-local mean, anisotropic diffusion, total variation minimization, and multi-scale transform coefficient thresholding for both middle and high levels of Gaussian noise cases.