Image Denoising Using Weighted Averaging
Dengwen Zhou, Shen Xiaoliu · 2009
Guleryuz proposed a simple and powerful image denoising algorithm using weighted averaging based on DCTs. The shortcomings of Guleryuz's method are that it needs to train two threshold parameters and its denoising ability deteriorates when noise level becomes high. In this paper, we give a method which trains the two parameters. We also improve Guleryuz's method via local Wiener filtering. Our method only needs to train a threshold parameter and also performs significantly better than Guleryuz's method.