{"action":"closed","number":367,"pull_request":{"url":"https://api.github.com/repos/Itseez/opencv_contrib/pulls/367","id":44515721,"html_url":"https://github.com/Itseez/opencv_contrib/pull/367","diff_url":"https://github.com/Itseez/opencv_contrib/pull/367.diff","patch_url":"https://github.com/Itseez/opencv_contrib/pull/367.patch","issue_url":"https://api.github.com/repos/Itseez/opencv_contrib/issues/367","number":367,"state":"closed","locked":false,"title":"Add rolling guidance filter","user":{"login":"zhou-chao","id":9026119,"avatar_url":"https://avatars.githubusercontent.com/u/9026119?v=3","gravatar_id":"","url":"https://api.github.com/users/zhou-chao","html_url":"https://github.com/zhou-chao","followers_url":"https://api.github.com/users/zhou-chao/followers","following_url":"https://api.github.com/users/zhou-chao/following{/other_user}","gists_url":"https://api.github.com/users/zhou-chao/gists{/gist_id}","starred_url":"https://api.github.com/users/zhou-chao/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/zhou-chao/subscriptions","organizations_url":"https://api.github.com/users/zhou-chao/orgs","repos_url":"https://api.github.com/users/zhou-chao/repos","events_url":"https://api.github.com/users/zhou-chao/events{/privacy}","received_events_url":"https://api.github.com/users/zhou-chao/received_events","type":"User","site_admin":false},"body":"Rolling guidance filter [1], along with its test code, is added.\r\n\r\nRolling guidance filter is the first of its kind for texture smoothing and edge preserving. It generates unique and practically very useful scale-aware local operators that remove texture under a novel scale measure, which cannot be achieved similarly by any prior filters. Rolling guidance filter also has many advantages - for example, its code is surprisingly simple and the running speed is very fast. It can benefit 3D geometry processing, feature extraction, image and video coding, and many other tasks in computer vision. The following is a simple example. The texture is removed efficiently and effectively by rolling guidance filter while main edges are preserved.\r\n\r\nThis method was accepted in ECCV 2014 as an oral presentation with acceptance rate around 3%. It is now used by many researchers and developers in this field.\r\n\r\n<table>\r\n<tr>\r\n<td><img src=https://cloud.githubusercontent.com/assets/9026119/9754492/4f746038-56fc-11e5-96c4-3a560fc49e7d.png></td>\r\n<td><img src=https://cloud.githubusercontent.com/assets/9026119/9754495/50655e2a-56fc-11e5-952e-40fa17a2681d.png></td>\r\n<td><img src=https://cloud.githubusercontent.com/assets/9026119/9754496/5141f646-56fc-11e5-971c-d45d21a21cae.png></td>\r\n</tr>\r\n<tr>\r\n<td>Input</td>\r\n<td>Bilateral Filter</td>\r\n<td>Rolling Guidance Filter</td>\r\n</tr>\r\n</table>\r\n                \r\nFor more details, please visit http://www.cse.cuhk.edu.hk/leojia/projects/rollguidance/.\r\n\r\n[1] Zhang, Q., Shen, X., Xu, L., & Jia, J. (2014). Rolling guidance filter. In Computer Vision–ECCV 2014 (pp. 815-830). Springer International 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