Context-based denoising of images using iterative wavelet thresholding

In this paper, we propose a spatially adaptive wavelet thresholding method using a context model that has been inspired by our prior work on image coding. The proposed context model relies on an estimation of the weighted variance in a local window of scale and space. Appropriately chosen weights are used to model the predominant correlations for a reliable statistical estimation. By iterating the context-based thresholding operation, a more accurate reconstruction can be achieved. Experimental results show that our proposed method yields significantly improved visual quality as well as lower mean squared error compared to the best recently published results in the denoising literature.