Rouphael, RobinNoizet, MathieuPrévost, StéphanieDeleau, HervéSteffenel, Luiz-AngeloLucas, LaurentSauvage, BasileHasic-Telalovic, Jasminka2022-04-222022-04-222022978-3-03868-171-71017-4656https://doi.org/10.2312/egp.20221011https://diglib.eg.org:443/handle/10.2312/egp20221011Spectral Monte Carlo (MC) rendering is still to be largely adopted partially due to the specific noise, called color noise, induced by wavelength-dependent phenomenons. Motivated by the recent advances in Monte Carlo noise reduction using Deep Learning, we propose to apply the same approach to color noise. Our implementation and training managed to reconstruct a noise-free output while conserving high-frequency details despite a loss of contrast. To address this issue, we designed a three-step pipeline using the contribution of a secondary denoiser to obtain high-quality results.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies --> Ray tracing; Neural networks; Image processingComputing methodologiesRay tracingNeural networksImage processing10.2312/egp.2022101125-262 pages