Adaptive Image Denoising Algorithm Based on Correlativity

A new adaptive weighted image denoising algorithm is proposed, which uses the correlativity between the original image and the mean image. The first process distinguishes the noise spots according to the relations between the grey coefficients of incidence matrix which are calculated between the noise image and the mean image and the threshold value TH. The second one calculates the values of the noise spot according to the correlativity coefficients which is calculated between the noise image and the mean image on the 3 × 3 template and realizes the adaptive weighted image denoising. Finally, we choice the signal-to-noise ratio, Peak Signal to Noise Ratio and the mean error to test the denoising effect under the different noise level. The results show that this method may reduce the image fuzziness, preserve the integrity of edge and detail information, and has the good denoising effect.