Neural James-Stein Combiner for Unbiased and Biased Renderings

Jeongmin Gu 1 , Jose A. Iglesias-Guitian 2 , Bochang Moon 1

Gwangju Institute of Science and Technology 1 , University of A Coruña - CITIC 2

ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2022)

Neural James-Stein Combiner for Unbiased and Biased Renderings
Numerical convergence of the learning-based denoisers, KPCN [Bako et al. 2017] and AFGSA [Yu et al. 2021], with and without our technique. We report the relative 𝑙 2 errors [Rousselle et al. 2011] of the tested techniques from 16 to 2K samples per pixel (spp). The state-of-the-art denoisers show much lower errors than their input, i.e., path tracing (PT), for small sample counts (e.g., 16), but their improvements become minor (c) or disappear ((a), (d), and (e)) for larger sample counts due to their slow convergence rates, except for the Veach-Ajar that contains fireflies. Our technique helps the denoisers have lower errors than their unbiased inputs, and this dominance property leads to significantly improved convergence rates of the input denoisers.


Unbiased rendering algorithms such as path tracing produce accurate images given a huge number of samples, but in practice, the techniques often leave visually distracting artifacts (i.e., noise) in their rendered images due to a limited time budget. A favored approach for mitigating the noise problem is applying learning-based denoisers to unbiased but noisy rendered images and suppressing the noise while preserving image details. However, such denoising techniques typically introduce a systematic error, i.e., the denoising bias, which does not decline as rapidly when increasing the sample size, unlike the other type of error, i.e., variance. It can technically lead to slow numerical convergence of the denoising techniques. We propose a new combination framework built upon the James-Stein (JS) estimator, which merges a pair of unbiased and biased rendering images, e.g., a path-traced image and its denoised result. Unlike existing post-correction techniques for image denoising, our framework helps an input denoiser have lower errors than its unbiased input without relying on accurate estimation of per-pixel denoising errors. We demonstrate that our framework based on the well-established JS theories allows us to improve the error reduction rates of state-of-the-art learning-based denoisers more robustly than recent post-denoisers.


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