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DTSTAMP:20240214T070241Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_446@linklings.com
SUMMARY:Input-Dependent Uncorrelated Weighting for Monte Carlo Denoising
DESCRIPTION:Technical Papers\n\nJonghee Back (Gwangju Institute of Science
  and Technology), Binh-Son Hua (Trinity College Dublin), Toshiya Hachisuka
  (University of Waterloo), and Bochang Moon (Gwangju Institute of Science 
 and Technology)\n\nImage-space denoising techniques have been widely emplo
 yed in Monte Carlo rendering, typically blending neighboring pixel estimat
 es using a denoising kernel. It is widely recognized that a kernel should 
 be adapted to characteristics of the input pixel estimates in order to ens
 ure robustness to diverse image features and amount of noise. Denoising wi
 th such an input-dependent kernel, however, can introduce a bias that make
 s the denoised estimate even less accurate than the noisy input estimate. 
 Consequently, it has been considered essential to balance the bias introdu
 ced by denoising and the reduction of noise. We propose a new framework to
  define an input-dependent kernel that departs from the existing approache
 s based on error estimation or supervised learning. Rather than seeking an
  optimal bias-noise balance as in those existing approaches, we propose to
  constrain the amount of bias introduced by denoising. Such a constraint i
 s made possible by the concept of uncorrelated statistics, which has never
  been applied for denoising. By designing an input-dependent kernel with u
 ncorrelated weights against the input pixel estimates, our denoising kerne
 l can reduce data-dependent noise with a negligible amount of bias in most
  cases. We demonstrate the effectiveness of our method for various scenes.
 \n\nRegistration Category: Full Access, Enhanced Access, Trade Exhibitor, 
 Experience Hall Exhibitor
URL:https://asia.siggraph.org/2023/full-program?id=papers_446&sess=sess209
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