Tsai, Yun-TaSteinberger, MarkusPajak, DawidPulli, KariIngo Wald and Jonathan Ragan-Kelley2015-07-062015-07-062014978-3-905674-60-62079-8679https://doi.org/10.2312/hpg.20141094https://diglib.eg.org:443/handle/10.2312/hpg.20141094Collaborative 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.I.4.3 [Image Processing and Computer Vision]EnhancementFiltering