{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T22:07:00Z","timestamp":1791324420920,"version":"4.3.1"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2015,7,27]],"date-time":"2015-07-27T00:00:00Z","timestamp":1437955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2015,7,27]]},"abstract":"<jats:p>\n            The most successful approaches for filtering Monte Carlo noise use feature-based filters (e.g., cross-bilateral and cross non-local means filters) that exploit additional scene features such as world positions and shading normals. However, their main challenge is finding the optimal weights for each feature in the filter to reduce noise but preserve scene detail. In this paper, we observe there is a complex relationship between the noisy scene data and the ideal filter parameters, and propose to\n            <jats:italic>learn<\/jats:italic>\n            this relationship using a nonlinear regression model. To do this, we use a multilayer perceptron neural network and combine it with a matching filter during both training and testing. To use our framework, we first train it in an offline process on a set of noisy images of scenes with a variety of distributed effects. Then at run-time, the trained network can be used to drive the filter parameters for new scenes to produce filtered images that approximate the ground truth. We demonstrate that our trained network can generate filtered images in only a few seconds that are superior to previous approaches on a wide range of distributed effects such as depth of field, motion blur, area lighting, glossy reflections, and global illumination.\n          <\/jats:p>","DOI":"10.1145\/2766977","type":"journal-article","created":{"date-parts":[[2015,7,28]],"date-time":"2015-07-28T12:26:38Z","timestamp":1438086398000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":130,"title":["A machine learning approach for filtering Monte Carlo noise"],"prefix":"10.1145","volume":"34","author":[{"given":"Nima Khademi","family":"Kalantari","sequence":"first","affiliation":[{"name":"University of California, Santa Barbara"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve","family":"Bako","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pradeep","family":"Sen","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2015,7,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/882262.882318"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2011.01996.x"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/040616024"},{"key":"e_1_2_2_4_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2392--2399","author":"Burger H."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2011.01854.x"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/964965.808590"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2010.77"},{"key":"e_1_2_2_8_1","volume-title":"Proceedings of High Performance Graphics (HPG), 67--75","author":"Dammertz H."},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2532708"},{"key":"e_1_2_2_10_1","unstructured":"Drucker H. Burges C. J. C. Kaufman L. Smola A. and Vapnik V. 1997. Support vector regression machines. In Advances in Neural Information Processing Systems 9 MIT Press 155--161.  Drucker H. Burges C. J. C. Kaufman L. Smola A. and Vapnik V. 1997. Support vector regression machines. In Advances in Neural Information Processing Systems 9 MIT Press 155--161."},{"key":"e_1_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Dutr\u00e9 P. Bala K. Bekaert P. and Shirley P. 2006. Advanced Global Illumination. AK Peters Ltd.   Dutr\u00e9 P. Bala K. Bekaert P. and Shirley P. 2006. Advanced Global Illumination. AK Peters Ltd.","DOI":"10.1201\/b10632"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1531326.1531399"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2070781.2024214"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1944846.1944849"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/280814.280816"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1360612.1360632"},{"key":"e_1_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Hastie T. Tibshirani R. Friedman J. Hastie T. Friedman J. and Tibshirani R. 2009. The elements of statistical learning vol. 2. Springer.  Hastie T. Tibshirani R. Friedman J. Hastie T. Friedman J. and Tibshirani R. 2009. The elements of statistical learning vol. 2. Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2011.01988.x"},{"key":"e_1_2_2_19_1","first-page":"134","article-title":"Optimizing path tracing using noise reduction filters","volume":"1995","author":"Jensen H. W.","year":"1995","journal-title":"Winter School of Computer Graphics (WSCG)"},{"key":"e_1_2_2_20_1","volume-title":"Realistic Image Synthesis Using Photon Mapping","author":"Jensen H. W."},{"key":"e_1_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Kalantari N. K. and Sen P. 2013. Removing the noise in Monte Carlo rendering with general image denoising algorithms. Computer Graphics Forum 32 2pt1 93--102.  Kalantari N. K. and Sen P. 2013. Removing the noise in Monte Carlo rendering with general image denoising algorithms. Computer Graphics Forum 32 2pt1 93--102.","DOI":"10.1111\/cgf.12029"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/2383847.2383883"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015751"},{"key":"e_1_2_2_24_1","series-title":"Lecture Notes in Computer Science","volume-title":"-R","author":"Le Cun Y.","year":"1998"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/38.55149"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2010324.1964950"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366213"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/318009.318015"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366182"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2461947"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601113"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/37402.37410"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2641762"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2009.01364.x"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/1618452.1618486"},{"key":"e_1_2_2_36_1","unstructured":"Pharr M. and Humphreys G. 2010. Physically Based Rendering: From Theory to Implementation second ed. Morgan Kaufmann Publishers Inc. San Francisco CA USA.   Pharr M. and Humphreys G. 2010. Physically Based Rendering: From Theory to Implementation second ed. Morgan Kaufmann Publishers Inc. San Francisco CA USA."},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2462009"},{"key":"e_1_2_2_38_1","volume-title":"IEEE International Conference on Neural Networks, 586--591","volume":"1","author":"Riedmiller M."},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2070781.2024193"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366214"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12219"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/192161.192189"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/1283900.1283909"},{"key":"e_1_2_2_45_1","volume-title":"Tech. Rep. EECE-TR-11-0004","author":"Sen P.","year":"2011"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2167076.2167083"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1944745.1944747"},{"key":"e_1_2_2_48_1","first-page":"183","article-title":"Discrepancy as a quality measure for sample distributions","volume":"91","author":"Shirley P.","year":"1991","journal-title":"Proc. Eurographics"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176345632"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1018628609742"},{"key":"e_1_2_2_51_1","volume-title":"Sixth International Conference on Computer Vision, 839--846","author":"Tomasi C."},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/218380.218498"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/258734.258775"},{"key":"e_1_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCG.2005.31"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2766977","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2766977","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T05:43:01Z","timestamp":1750225381000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2766977"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,7,27]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2015,7,27]]}},"alternative-id":["10.1145\/2766977"],"URL":"https:\/\/doi.org\/10.1145\/2766977","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,7,27]]},"assertion":[{"value":"2015-07-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}