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.