(The Eurographics Association, 2014) Tsai, Yun-Ta; Steinberger, Markus; Pajak, Dawid; Pulli, Kari; Ingo Wald and Jonathan Ragan-Kelley
Collaborative filtering collects similar patches, jointly filters them, and scatters the output back to input patches; each pixel gets a contribution from each patch that overlaps with it, allowing signal reconstruction from highly corrupted data. Exploiting self-similarity, however, requires finding matching image patches, which is an expensive operation. We propose a GPU-friendly approximated-nearest-neighbor algorithm that produces high-quality results for any type of collaborative filter. We evaluate our ANN search against state-of-the-art ANN algorithms in several application domains. Our method is orders of magnitudes faster, yet provides similar or higher-quality results than the previous work.