Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings
We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state‐of‐the‐art performance on a wide range of scenes. We analyze existing approaches from a theoretical and empirical point of view, relating the strengths and limit...
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| Published in | Computer graphics forum Vol. 35; no. 4; pp. 107 - 117 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.07.2016
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| Subjects | |
| Online Access | Get full text |
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| Abstract | We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state‐of‐the‐art performance on a wide range of scenes. We analyze existing approaches from a theoretical and empirical point of view, relating the strengths and limitations of their corresponding components with an emphasis on production requirements. The observations of our analysis instruct the design of our new filter that offers high‐quality results and stable performance. A key observation of our analysis is that using auxiliary buffers (normal, albedo, etc.) to compute the regression weights greatly improves the robustness of zero‐order models, but can be detrimental to first‐order models. Consequently, our filter performs a first‐order regression leveraging a rich set of auxiliary buffers only when fitting the data, and, unlike recent works, considers the pixel color alone when computing the regression weights. We further improve the quality of our output by using a collaborative denoising scheme. Lastly, we introduce a general mean squared error estimator, which can handle the collaborative nature of our filter and its nonlinear weights, to automatically set the bandwidth of our regression kernel. |
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| AbstractList | We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state‐of‐the‐art performance on a wide range of scenes. We analyze existing approaches from a theoretical and empirical point of view, relating the strengths and limitations of their corresponding components with an emphasis on production requirements. The observations of our analysis instruct the design of our new filter that offers high‐quality results and stable performance. A key observation of our analysis is that using auxiliary buffers (normal, albedo, etc.) to compute the regression weights greatly improves the robustness of zero‐order models, but can be detrimental to first‐order models. Consequently, our filter performs a first‐order regression leveraging a rich set of auxiliary buffers only when fitting the data, and, unlike recent works, considers the pixel color alone when computing the regression weights. We further improve the quality of our output by using a collaborative denoising scheme. Lastly, we introduce a general mean squared error estimator, which can handle the collaborative nature of our filter and its nonlinear weights, to automatically set the bandwidth of our regression kernel. |
| Author | Rousselle, Fabrice Iglesias-Guitián, José A. Adler, David Bitterli, Benedikt Mitchell, Kenny Novák, Jan Jarosz, Wojciech Moon, Bochang |
| Author_xml | – sequence: 1 givenname: Benedikt surname: Bitterli fullname: Bitterli, Benedikt organization: Disney Research – sequence: 2 givenname: Fabrice surname: Rousselle fullname: Rousselle, Fabrice organization: Disney Research – sequence: 3 givenname: Bochang surname: Moon fullname: Moon, Bochang organization: Disney Research – sequence: 4 givenname: José A. surname: Iglesias-Guitián fullname: Iglesias-Guitián, José A. organization: Disney Research – sequence: 5 givenname: David surname: Adler fullname: Adler, David organization: Walt Disney Animation Studios – sequence: 6 givenname: Kenny surname: Mitchell fullname: Mitchell, Kenny organization: Disney Research – sequence: 7 givenname: Wojciech surname: Jarosz fullname: Jarosz, Wojciech organization: Dartmouth College – sequence: 8 givenname: Jan surname: Novák fullname: Novák, Jan organization: Disney Research |
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| Cites_doi | 10.1109/ICCV.1998.710815 10.1145/2532708 10.1109/MCG.2005.31 10.1111/cgf.12004 10.1111/cgf.12592 10.1145/2766992 10.1145/2766977 10.1145/1015706.1015777 10.1145/2167076.2167083 10.1111/cgf.12029 10.1145/2897824.2925936 10.1111/cgf.12219 10.1111/j.1467-8659.2011.01996.x 10.1109/LSP.2009.2027669 10.1145/2641762 10.1111/cgf.12587 10.1109/18.382009 10.1145/318009.318015 10.1214/aos/1176345632 10.1109/TIP.2006.888330 10.1145/1015706.1015778 10.1137/040616024 10.1561/0600000020 10.1109/TIP.2002.801126 10.1007/s11263-007-0052-1 10.1111/cgf.12829 |
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| Copyright | 2016 The Author(s) Computer Graphics Forum © 2016 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2016 The Eurographics Association and John Wiley & Sons Ltd. |
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| References | Moon B., Iglesias-Guitian J.A., Yoon S.-E., Mitchell K.: Adaptive rendering with linear predictions. ACM Trans. Graph. (Proc. SIGGRAPH) 34, 4 (July 2015), 121:1-121:11. 2, 5, 7 Li T.-M., Wu Y.-T., Chuang Y.-Y.: Sure-based optimization for adaptive sampling and reconstruction. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 31, 6 (Nov. 2012), 194:1-194:9. 2, 4 Buades A., Coll B., Morel J.-M.: Nonlocal image and movie denoising. International Journal of Computer Vision 76, 2 (2007), 123-139. 8 Zwicker M., Jarosz W., Lehtinen J., Moon B., Ramamoorthi R., Rousselle F., Sen P., Soler C., Yoon S.-E.: Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering. Comp. Graph. Forum (Proc. Eurographics) 34, 2 (May 2015), 667-681. 1, 2 Moon B., Jun J.Y., Lee J., Kim K., Hachisuka T., Yoon S.-E.: Robust image denoising using a virtual flash image for Monte Carlo ray tracing. Comp. Graph. Forum 32, 1 (2013), 139-151. 2 Takeda H., Farsiu S., Milanfar P.: Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing 16, 2 (Feb 2007), 349-366. 3 Van de Ville D., Kocher M.: Sure-based non-local means. IEEE Signal Process. Lett. 16, 11 (2009), 973-976. 2 Buades A., Coll B., Morel J.-M.: A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation 4, 2 (2005), 490-530. 2, 3, 8 Eisemann E., Durand F.: Flash photography enhancement via intrinsic relighting. ACM Trans. Graph. (Proc. SIGGRAPH) 23, 3 (Aug. 2004), 673-678. 2 Sen P., Darabi S.: On filtering the noise from the random parameters in Monte Carlo rendering. ACM Trans. Graph. 31, 3 (June 2012), 18:1-18:15. 2, 4 Paris S., Kornprobst P., Tumblin J., Durand F.: Bilateral filtering: Theory and applications. 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Comp. Graph. Forum (Proc. Eurographics) 32, 2pt1 (2013), 93-102. 2 Bauszat P., Eisemann M., Magnor M.: Guided image filtering for interactive high-quality global illumination. Comp. Graph. Forum 30, 4 (2011), 1361-1368. 2, 3 Donoho D.L.: De-noising by soft-thresholding. Information Theory, IEEE Transactions on 41, 3 (1995), 613-627. 2 Kalantari N.K., Bako S., Sen P.: A machine learning approach for filtering Monte Carlo noise. ACM Trans. Graph. (Proc. SIGGRAPH) 34, 4 (July 2015), 122:1-122:12. 2, 3, 4, 8 Rousselle F., Knaus C., Zwicker M.: Adaptive sampling and reconstruction using greedy error minimization. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 30, 6 (Dec. 2011), 159:1-159:12. 2, 4 Moon B., Carr N., Yoon S.-E.: Adaptive rendering based on weighted local regression. ACM Trans. Graph. 33, 5 (Sept. 2014), 170:1-170:14. 2, 3, 4, 8 Delbracio M., Musé P., Buades A., Chauvier J., Phelps N., Morel J.-M.: Boosting Monte Carlo rendering by ray histogram fusion. ACM Trans. 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| References_xml | – reference: Van de Ville D., Kocher M.: Sure-based non-local means. IEEE Signal Process. Lett. 16, 11 (2009), 973-976. 2 – reference: Li T.-M., Wu Y.-T., Chuang Y.-Y.: Sure-based optimization for adaptive sampling and reconstruction. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 31, 6 (Nov. 2012), 194:1-194:9. 2, 4 – reference: Bauszat P., Eisemann M., Eisemann E., Magnor M.: General and robust error estimation and reconstruction for Monte Carlo rendering. Computer Graphics Forum 34, 2 (2015), 597-608. 4, 5 – reference: Delbracio M., Musé P., Buades A., Chauvier J., Phelps N., Morel J.-M.: Boosting Monte Carlo rendering by ray histogram fusion. ACM Trans. Graph. 33, 1 (Feb. 2014), 8:1-8:15. 2, 3, 8, 9 – reference: Petschnigg G., Szeliski R., Agrawala M., Cohen M., Hoppe H., Toyama K.: Digital photography with flash and no-flash image pairs. ACM Trans. Graph. (Proc. SIGGRAPH) 23, 3 (Aug. 2004), 664-672. 2 – reference: Rousselle F., Manzi M., Zwicker M.: Robust denoising using feature and color information. Comp. Graph. Forum (Proc. of Pacific Graphics) 32, 7 (2013), 121-130. 2, 3, 4, 6, 7, 8 – reference: Xu R., Pattanaik S.: A novel Monte Carlo noise reduction operator. IEEE Comput. Graph. Appl. 25, 2 (2005), 31-35. 2 – reference: Takeda H., Farsiu S., Milanfar P.: Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing 16, 2 (Feb 2007), 349-366. 3 – reference: Kalantari N.K., Bako S., Sen P.: A machine learning approach for filtering Monte Carlo noise. ACM Trans. Graph. (Proc. SIGGRAPH) 34, 4 (July 2015), 122:1-122:12. 2, 3, 4, 8 – reference: Moon B., Iglesias-Guitian J.A., Yoon S.-E., Mitchell K.: Adaptive rendering with linear predictions. ACM Trans. Graph. (Proc. SIGGRAPH) 34, 4 (July 2015), 121:1-121:11. 2, 5, 7 – reference: Zwicker M., Jarosz W., Lehtinen J., Moon B., Ramamoorthi R., Rousselle F., Sen P., Soler C., Yoon S.-E.: Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering. Comp. Graph. Forum (Proc. Eurographics) 34, 2 (May 2015), 667-681. 1, 2 – reference: Donoho D.L.: De-noising by soft-thresholding. Information Theory, IEEE Transactions on 41, 3 (1995), 613-627. 2 – reference: Overbeck R.S., Donner C., Ramamoorthi R.: Adaptive wavelet rendering. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 28, 5 (2009), 140-1. 2 – reference: Rousselle F., Knaus C., Zwicker M.: Adaptive rendering with non-local means filtering. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 31, 6 (Nov. 2012), 195:1-195:11. 2, 3, 4, 6, 8 – reference: Buades A., Coll B., Morel J.-M.: Nonlocal image and movie denoising. International Journal of Computer Vision 76, 2 (2007), 123-139. 8 – reference: Sen P., Darabi S.: On filtering the noise from the random parameters in Monte Carlo rendering. ACM Trans. Graph. 31, 3 (June 2012), 18:1-18:15. 2, 4 – reference: Bauszat P., Eisemann M., Magnor M.: Guided image filtering for interactive high-quality global illumination. Comp. Graph. Forum 30, 4 (2011), 1361-1368. 2, 3 – reference: Kalantari N.K., Sen P.: Removing the noise in monte carlo rendering with general image denoising algorithms. Comp. Graph. Forum (Proc. Eurographics) 32, 2pt1 (2013), 93-102. 2 – reference: Moon B., Jun J.Y., Lee J., Kim K., Hachisuka T., Yoon S.-E.: Robust image denoising using a virtual flash image for Monte Carlo ray tracing. Comp. Graph. Forum 32, 1 (2013), 139-151. 2 – reference: Elad M.: On the origin of the bilateral filter and ways to improve it. 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| Snippet | We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state‐of‐the‐art... We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state-of-the-art... |
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| SubjectTerms | Algorithms Buffers Categories and Subject Descriptors (according to ACM CCS) Collaboration Computer graphics Computer simulation I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism-Raytracing I.4.3 [Computer Graphics]: Enhancement-Filtering Monte Carlo methods Monte Carlo simulation Noise reduction Regression Regression analysis Rendering Studies |
| Title | Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings |
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