Non-local Neighbor Embedding Image Denoising Algorithm in Sparse Domain

To get better denoising results, the prior knowledge of nature images should be taken into account to regularize the ill-posed inverse problem. In this paper, we propose an image denoising algorithm via non-local similar neighbor embedding in sparse domain. Firstly, a local statistical feature, namely histograms of oriented gradients of image patches is used to perform the clustering, and then the whole training data set is partitioned into a set of subsets which have similar local geometric structures and the centroid of each subset is also obtained. Secondly, we apply the principal component analysis (PCA) to learn the compact sub-dictionary for each cluster. Next, through sparse coding over the sub-dictionary and neighborhood selecting, the image patch to be synthesized can be approximated by its top k neighbors. The extensive experimental results validate the effective of the proposed method both in PSNR and visual perception. Key words: image denoising, non-local prior, sparse representation, neighbor embedding