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Removing Monte Carlo noise using a Sobel operator and a guided image filter

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Abstract

In this study, a novel adaptive rendering approach is proposed to remove Monte Carlo noise while preserving image details through a feature-based reconstruction. First, noise in the additional features is removed using a guided image filter that reduces the impact of noisy features involving strong motion blur or depth of field. The Sobel operator is then employed to recognize the geometric structures by robustly computing a gradient buffer for each feature. Given the gradient information for high-dimensional features, we compute the optimal filter parameters using a data-driven method. Finally, an error analysis is derived through a two-step smoothing strategy to produce a smooth image and guide the adaptive sampling process. Experimental results indicate that our approach outperforms state-of-the-art methods in terms of visual image quality and numerical error.

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References

  1. Li, T.M., Wu, Y.T., Chuang, Y.Y.: SURE-based optimization for adaptive sampling and reconstruction. ACM Trans. Graph. 31(6), 194:1–194:9 (2012)

    Google Scholar 

  2. Rousselle, F., Manzi, M., Zwicker, M.: Robust denoising using feature and color information. Comput. Graph. Forum 32(7), 121–130 (2013)

    Article  Google Scholar 

  3. Rousselle, F., Knaus, C., Zwicker, M.: Adaptive sampling and reconstruction using greedy error minimization. ACM Trans. Graph. 30(6), 6:1–6:11 (2011)

    Article  Google Scholar 

  4. Moon, B., Carr, N., Yoon, S.: Adaptive rendering based on weighted local regression. ACM Trans. Graph. 33(5), 170:1–170:14 (2014)

    Article  Google Scholar 

  5. Kalantari, N.K., Bako, S., Sen, P.: A machine learning approach for filtering Monte Carlo noise. ACM Trans. Graph. 34(4), 122:1–122:12 (2015)

    Article  Google Scholar 

  6. Bauszat, P., Eisemann, M., Eisemann, E.: General and robust error estimation and reconstruction for Monte Carlo rendering. Comput. Graph. Forum 34(2), 597–608 (2015)

    Article  Google Scholar 

  7. He, K., Sun, J., Tang, X.: Guided Image Filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(6), 1397–1409 (2010)

    Article  Google Scholar 

  8. Kajiya, J.T.: The rendering equation. In: ACM SIGGRAPH, pp. 143–150 (1986)

  9. Hachisuka, T., Jarosz, W., Weistroffer, R.P., Dale, K.: Multidimensional adaptive sampling and reconstruction for ray tracing. ACM Trans. Graph. 27(3), 33:1–33:10 (2008)

    Article  Google Scholar 

  10. Durand, F., Holzschuch, N., Soler, C., Chan, E., Sillion, F.X.: A frequency analysis of light transport. ACM Trans. Graph. 24(3), 1115–1126 (2005)

    Article  Google Scholar 

  11. Soler, C., Subr, K., Durand, F., Holzschuch, N., Sillion, F.: Fourier depth of field. ACM Trans. Graph. 28(2), 18:1–18:12 (2009)

    Article  Google Scholar 

  12. Egan, K., Tseng, Y.T., Durand, F., Holzschuch, N.: Frequency analysis and sheared reconstruction for rendering motion blur. ACM Trans Graph 28(3), 93:1–93:13 (2009)

    Article  Google Scholar 

  13. Egan, K., Hecht, F., Durand, F., Ramamoorthi, R.: Frequency analysis and sheared filtering for shadow light fields of complex occluders. ACM Trans. Graph. 30(2), 9:1–9:13 (2011)

    Article  Google Scholar 

  14. Egan, K., Durand, F., Ramamoorthi, R.: Practical filtering for efficient ray-traced directional occlusion. ACM Trans. Graph. 30(6), 180:1–180:10 (2011)

    Article  Google Scholar 

  15. Belcour, l, Soler, C., Subr, K., Holzschuch, N., Durand, F.: 5D covariance tracing for efficient defocus and motion blur. ACM Trans. Graph. 32(3), 31:1–31:18 (2011)

    MATH  Google Scholar 

  16. Lehtinen, J., Aila, T., Chen, J., Laine, S., Durand, F.: Temporal light field reconstruction for rendering distribution effects. ACM Trans. Graph. 31(4), 55:1–55:10 (2012)

    Article  Google Scholar 

  17. Lehtinen, J., Aila, T., Laine, S., Durand, F.: Reconstructing the indirect light field for global illumination. ACM Trans Graph 30(4), 51:1–51:12 (2011)

    Article  Google Scholar 

  18. Kettunen, M., Manzi, M., Aittala, M., Lehtinen, J.: Gradient-domain path tracing. ACM Trans. Graph. 34(4), 123:1–123:13 (2015)

    Article  MATH  Google Scholar 

  19. Manzi, M., Vicini, D., Zwicker, M.: Regularizing image reconstruction for gradient-domain rendering with feature patches. Comput. Graph. Forum 35(2), 263–273 (2016)

    Article  Google Scholar 

  20. Sen, P., Darabi, S.: Compressed rendering: a rendering application of compressed sensing. IEEE Trans. Vis. Comput. Graph. 17(4), 487–499 (2011)

    Article  Google Scholar 

  21. Liu, X.D., WU, J.Z., Zheng, C.W.: KD-tree based parallel adaptive rendering. The Visual Computer 28(6–8), 613–623 (2012)

    Article  Google Scholar 

  22. Mitchell, D.P.: Generating antialiased images at low sampling densities. In: Computer Graphics Proceedings, Annual Conference Series, ACM SIGGRAPH, vol. 21, pp. 65-72. ACM, Anaheim (1987)

  23. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In Proceedings of the International Conference on Computer Vision, pp. 839–846 (1998)

  24. Buades, A., Coll, B., Morel, J.M: A non-local algorithm for image denoising. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 60-65 (2005)

  25. Rousselle, F., Knaus, C., Zwicker, M.: Adaptive rendering with non-local means filtering. ACM Trans. Graph. 31(6), 195:1–195:9 (2012)

    Article  Google Scholar 

  26. Overbeck, R.S., Donner, C., Ramamoorthi, R.: Adaptive wavelet rendering. ACM Trans. Graph. 28(5), 140:1–140:12 (2009)

    Article  Google Scholar 

  27. Kalantari, N.K., Sen, P.: Removing the noise in Monte Carlo rendering with general image denoising algorithm. Comput. Graph. Forum 32(2), 93–102 (2013)

    Article  Google Scholar 

  28. Sen, P., Darabi, S.: On Filtering the Noise from the Random parameters in Monte Carlo Rendering. ACM Trans Graph 31(3), 18:1–18:14 (2012)

    Article  Google Scholar 

  29. Bauszat, P., Eisemann, M., John, S.: Sample-based manifold filtering for interactive global illumination and depth of field. Comput. Graph. Forum 34(1), 265–276 (2015)

    Article  Google Scholar 

  30. Liu, X.D., Zheng, C.W.: Adaptive cluster rendering via regression analysis. Vis. Comput. 31(1), 105–114 (2015)

    Article  Google Scholar 

  31. Liu, X.D., Zheng, C.W.: Parallel adaptive sampling and reconstruction using multi-scale and directional analysis. The Visual Computer 29(6–8), 501–511 (2013)

    Article  Google Scholar 

  32. Moon, B., Jun, J.Y., Lee, J.: Robust image denoising using a virtual flash image for Monte Carlo ray tracing. Comput. Graph. Forum 32(1), 139–151 (2013)

    Article  Google Scholar 

  33. Bitterli, B., Rousselle, F., Moon, B.: Nonlinearly weighted first-order regression for denoising Monte Carlo renderings. Comput. Graph. Forum 35(4), 107–117 (2016)

    Article  Google Scholar 

  34. Bauszat, P., Eisemann, M., Magnor, M.: Guided image filtering for interactive high-quality global illumination. Comput. Graph. Forum 30(4), 1361–1368 (2011)

    Article  Google Scholar 

  35. Delbracio, M., Muse, P., Buades, A., Chauvier, J.: Boosting Monte Carlo Rendering by ray histogram fusion. ACM Trans Graph 33(1), 8:1–8:15 (2014)

    Article  MATH  Google Scholar 

  36. Moon, B., Guitian, J.A., Yoon, S., Mitchell, K.: Adaptive rendering with linear predictions. ACM Trans. Graph. 34(4), 121:1–121:11 (2015)

    Article  Google Scholar 

  37. Moon, B., McDonagh, S., Mitchell, K., Gross, M.: Adaptive polynomial rendering. ACM Trans Graph 35(4), 40:1–40:11 (2016)

  38. Zwicker, M., Jarosz, W., Lehtinen, J., Moon, B.: Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering. Comput. Graph. Forum 34(2), 667–681 (2015)

    Article  Google Scholar 

  39. Ruppert, D., Wand, M.: Multivariate locally weighted least squares regression. The annals of statistics 22(3), 1346–1370 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  40. Pharr, M., Humphreys, G.: Physically Based Rendering: From Theory to Implementation. Morgan Kaufmann Publishers Inc., San Fransisco (2010)

    Google Scholar 

  41. Wu, F.K., Zheng, C.W.: Microfacet-based interference simulation for multilayer films. Graphical Models 78(6), 26–35 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, China

    Yu Liu

  2. Institute of Software, Chinese Academy of Sciences, Beijing, China

    Yu Liu, Changwen Zheng, Quan Zheng & Hongliang Yuan

Authors
  1. Yu Liu
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  2. Changwen Zheng
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  3. Quan Zheng
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  4. Hongliang Yuan
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Correspondence to Yu Liu.

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Liu, Y., Zheng, C., Zheng, Q. et al. Removing Monte Carlo noise using a Sobel operator and a guided image filter. Vis Comput 34, 589–601 (2018). https://doi.org/10.1007/s00371-017-1363-z

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  • DOI: https://doi.org/10.1007/s00371-017-1363-z

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