Image denoising using NL-means via smooth patch ordering

In our recent work we proposed an image denoising scheme based on reordering of the noisy image pixels to a one dimensional (1D) signal, and applying linear smoothing filters on it. This algorithm had two main limitations: 1) It did not take advantage of the distances between the noisy image patches, which were used in the reordering process; and 2) the smoothing filters required a separate training set to be learned from. In this work, we propose an image denoising algorithm, which applies similar permutations to the noisy image, but overcomes the above two shortcomings. We eliminate the need for learning filters by employing the nonlocal means (NL-means) algorithm. We estimate each pixel as a weighted average of noisy pixels in union of neighborhoods obtained from different global pixel permutations, where the weights are determined by distances between the patches. We show that the proposed scheme achieves results which are close to the state-of-the-art.