An improved image denoising algorithm based on weighted adaptive local bounds

In this paper we tackle the problem of image denoising. Given an image distorted by additive noise of unknown probability distribution, the intensity difference between the distorted and the original unknown images must be bounded. This idea is exploited here and we design a series of weighted adaptive local bounds based on local intensity information, such as the mean, variance, median and etc. The proposed method is tested and compared with other standard techniques, such as wavelet thresholding, for image denoising. Apart from the simplicity of implementation, the results are very encouraging, as far as both visual quality of the denoised images and quantitative metrics of improvement are concerned. More importantly it provides simultaneous desnoising of mixed noise, which is not obtainable by using single conventional denoising method.