Interactive neural cascade denoising for 1-spp Monte Carlo images

Abstract Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployme...
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Autor*in:	Chen, Yuankang [verfasserIn] Lu, Yifan Zhang, Xiaohua Xie, Nine

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Rendering Monte Carlo denoising Neural networks Ray tracing

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: The visual computer - Berlin : Springer, 1985, 39(2023), 8 vom: 12. Juli, Seite 3197-3210
Übergeordnetes Werk:	volume:39 ; year:2023 ; number:8 ; day:12 ; month:07 ; pages:3197-3210

Links:	Volltext

DOI / URN:	10.1007/s00371-023-02951-6

Katalog-ID:	SPR052784231

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520			\|a Abstract Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed.
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700	1		\|a Xie, Nine \|4 aut
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allfields	10.1007/s00371-023-02951-6 doi (DE-627)SPR052784231 (SPR)s00371-023-02951-6-e DE-627 ger DE-627 rakwb eng Chen, Yuankang verfasserin (orcid)0000-0001-6538-9342 aut Interactive neural cascade denoising for 1-spp Monte Carlo images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed. Rendering (dpeaa)DE-He213 Monte Carlo denoising (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Ray tracing (dpeaa)DE-He213 Lu, Yifan (orcid)0000-0001-6184-6200 aut Zhang, Xiaohua aut Xie, Nine aut Enthalten in The visual computer Berlin : Springer, 1985 39(2023), 8 vom: 12. Juli, Seite 3197-3210 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2023 number:8 day:12 month:07 pages:3197-3210 https://dx.doi.org/10.1007/s00371-023-02951-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2023 8 12 07 3197-3210
spelling	10.1007/s00371-023-02951-6 doi (DE-627)SPR052784231 (SPR)s00371-023-02951-6-e DE-627 ger DE-627 rakwb eng Chen, Yuankang verfasserin (orcid)0000-0001-6538-9342 aut Interactive neural cascade denoising for 1-spp Monte Carlo images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed. Rendering (dpeaa)DE-He213 Monte Carlo denoising (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Ray tracing (dpeaa)DE-He213 Lu, Yifan (orcid)0000-0001-6184-6200 aut Zhang, Xiaohua aut Xie, Nine aut Enthalten in The visual computer Berlin : Springer, 1985 39(2023), 8 vom: 12. Juli, Seite 3197-3210 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2023 number:8 day:12 month:07 pages:3197-3210 https://dx.doi.org/10.1007/s00371-023-02951-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2023 8 12 07 3197-3210
allfields_unstemmed	10.1007/s00371-023-02951-6 doi (DE-627)SPR052784231 (SPR)s00371-023-02951-6-e DE-627 ger DE-627 rakwb eng Chen, Yuankang verfasserin (orcid)0000-0001-6538-9342 aut Interactive neural cascade denoising for 1-spp Monte Carlo images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed. Rendering (dpeaa)DE-He213 Monte Carlo denoising (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Ray tracing (dpeaa)DE-He213 Lu, Yifan (orcid)0000-0001-6184-6200 aut Zhang, Xiaohua aut Xie, Nine aut Enthalten in The visual computer Berlin : Springer, 1985 39(2023), 8 vom: 12. Juli, Seite 3197-3210 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2023 number:8 day:12 month:07 pages:3197-3210 https://dx.doi.org/10.1007/s00371-023-02951-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2023 8 12 07 3197-3210
allfieldsGer	10.1007/s00371-023-02951-6 doi (DE-627)SPR052784231 (SPR)s00371-023-02951-6-e DE-627 ger DE-627 rakwb eng Chen, Yuankang verfasserin (orcid)0000-0001-6538-9342 aut Interactive neural cascade denoising for 1-spp Monte Carlo images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed. Rendering (dpeaa)DE-He213 Monte Carlo denoising (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Ray tracing (dpeaa)DE-He213 Lu, Yifan (orcid)0000-0001-6184-6200 aut Zhang, Xiaohua aut Xie, Nine aut Enthalten in The visual computer Berlin : Springer, 1985 39(2023), 8 vom: 12. Juli, Seite 3197-3210 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2023 number:8 day:12 month:07 pages:3197-3210 https://dx.doi.org/10.1007/s00371-023-02951-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2023 8 12 07 3197-3210
allfieldsSound	10.1007/s00371-023-02951-6 doi (DE-627)SPR052784231 (SPR)s00371-023-02951-6-e DE-627 ger DE-627 rakwb eng Chen, Yuankang verfasserin (orcid)0000-0001-6538-9342 aut Interactive neural cascade denoising for 1-spp Monte Carlo images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed. Rendering (dpeaa)DE-He213 Monte Carlo denoising (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Ray tracing (dpeaa)DE-He213 Lu, Yifan (orcid)0000-0001-6184-6200 aut Zhang, Xiaohua aut Xie, Nine aut Enthalten in The visual computer Berlin : Springer, 1985 39(2023), 8 vom: 12. Juli, Seite 3197-3210 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2023 number:8 day:12 month:07 pages:3197-3210 https://dx.doi.org/10.1007/s00371-023-02951-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2023 8 12 07 3197-3210
language	English
source	Enthalten in The visual computer 39(2023), 8 vom: 12. Juli, Seite 3197-3210 volume:39 year:2023 number:8 day:12 month:07 pages:3197-3210
sourceStr	Enthalten in The visual computer 39(2023), 8 vom: 12. Juli, Seite 3197-3210 volume:39 year:2023 number:8 day:12 month:07 pages:3197-3210
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Rendering Monte Carlo denoising Neural networks Ray tracing
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container_title	The visual computer
authorswithroles_txt_mv	Chen, Yuankang @@aut@@ Lu, Yifan @@aut@@ Zhang, Xiaohua @@aut@@ Xie, Nine @@aut@@
publishDateDaySort_date	2023-07-12T00:00:00Z
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language_de	englisch
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author	Chen, Yuankang
spellingShingle	Chen, Yuankang misc Rendering misc Monte Carlo denoising misc Neural networks misc Ray tracing Interactive neural cascade denoising for 1-spp Monte Carlo images
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topic_title	Interactive neural cascade denoising for 1-spp Monte Carlo images Rendering (dpeaa)DE-He213 Monte Carlo denoising (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Ray tracing (dpeaa)DE-He213
topic	misc Rendering misc Monte Carlo denoising misc Neural networks misc Ray tracing
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title	Interactive neural cascade denoising for 1-spp Monte Carlo images
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title_full	Interactive neural cascade denoising for 1-spp Monte Carlo images
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title_sort	interactive neural cascade denoising for 1-spp monte carlo images
title_auth	Interactive neural cascade denoising for 1-spp Monte Carlo images
abstract	Abstract Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Monte Carlo (MC) path tracing is known for its high fidelity and heavy computational cost.With the development of neural networks, the kernel-based post-processing method has succeeded in denoising noisy images under low sampling rates, but the complex network structure impedes its deployment in interactive applications. In this paper, we propose a lightweight cascaded network which progressively denoises 1-spp Monte Carlo images through both pixel and kernel prediction methods. A primary denoised image is generated by the pixel prediction network at the first stage, which is then fed to the kernel prediction network to obtain multi-resolution kernels. In addition, to take full advantage of the auxiliary buffers, we introduce a bilateral method during image reconstruction. Experimental results show that our approach achieves state-of-the-art denoising qualities for 1-spp images at an interactive frame speed. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	8
title_short	Interactive neural cascade denoising for 1-spp Monte Carlo images
url	https://dx.doi.org/10.1007/s00371-023-02951-6
remote_bool	true
author2	Lu, Yifan Zhang, Xiaohua Xie, Nine
author2Str	Lu, Yifan Zhang, Xiaohua Xie, Nine
ppnlink	254910734
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hochschulschrift_bool	false
doi_str	10.1007/s00371-023-02951-6
up_date	2024-07-03T14:46:28.810Z
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Interactive neural cascade denoising for 1-spp Monte Carlo images

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