Patch Matching for Image Denoising Using Neighborhood-Based Collaborative Filtering

We consider patch matching as a recommendation system problem and introduce a new patch-matching approach using nearest neighbor-based collaborative filtering (NN-CF). Our approach involves recommending similar patches to a query patch with the help of other similar patches in a noisy image or an external database. Using user-oriented and item-oriented formulations of NN-CF, we present two variations of CF-based patch-matching criterion. To demonstrate the superior matches found with our method, we apply the new patch-matching scheme to patch-based image denoising and evaluate its effect on the denoising performance. We test the methods on two data sets with varying background and image complexities and under different levels of noise. The proposed method not only improves robustness to patch matching but also provides a new formulation to seamlessly combine internal and external denoising.