Stochastic Modeling and Resolution-Free Rendering of Film Grain

The realistic synthesis and rendering of film grain is a crucial goal for many amateur and professional photographers and film-makers whose artistic works require the authentic feel of analog photography. The objective of this work is to propose an algorithm that reproduces the visual aspect of film grain texture on any digital image. Previous approaches to this problem either propose unrealistic models or simply blend scanned images of film grain with the digital image, in which case the result is inevitably limited by the quality and resolution of the initial scan. In this work, we introduce a stochastic model to approximate the physical reality of film grain, and propose a resolution-free rendering algorithm to simulate realistic film grain for any digital input image. By varying the parameters of this model, we can achieve a wide range of grain types. We demonstrate this by comparing our results with film grain examples from dedicated software, and show that our rendering results closely resemble these real film emulsions. In addition to realistic grain rendering, our resolution-free algorithm allows for any desired zoom factor, even down to the scale of the microscopic grains themselves

Um ambiente para desevonvoimento de algoritmos de amostragem e remoção de ruído

In the context of Monte Carlo rendering, although many sampling and denoising techniques have been proposed in the last few years, the case for which one should be used for a specific scene is still to be made. Moreover, developing a new technique has required selecting a particular rendering system, which makes the technique tightly coupled to the chosen renderer and limits the amount of scenes it can be tested on. In this work, we propose a renderer-agnostic framework for developing and benchmarking sampling and denoising techniques for Monte Carlo rendering. It decouples techniques from rendering systems by hiding the renderer details behind a general API. This improves productivity and allows for direct comparisons among techniques using scenes from different rendering systems. The proposed framework contains two main parts: a software development kit that helps users to develop and and test their techniques locally, and an online system that allows users to submit their techniques and have them automatically benchmarked on our servers. We demonstrate its effectiveness by using our API to instrument four rendering systems and a variety of Monte Carlo denoising techniques — including recent learning-based ones — and performing a benchmark across different rendering systems.No contexto de Monte Carlo rendering, apesar de diversas técnicas de amostragem e remoção de ruído tenham sido propostas nos últimos anos, aportar qual técnica deve ser usada para uma cena específica ainda é uma tarefa difícil. Além disso, desenvolver uma nova técnica requer escolher um renderizador em particular, o que torna a técnica dependente do renderizador escolhido e limita a quantidade de cenas disponíveis para testar a técnica. Neste trabalho, um framework para desenvolvimento e avaliação de técnicas de amostragem e remoção de ruído para Monte Carlo rendering é proposto. Ele permite desacoplar as técnicas dos renderizadores por meio de uma API genérica, promovendo a reprodutibilidade e permitindo comparações entre técnicas utilizando-se cenas de diferentes renderizadores. O sistema proposto contém duas partes principais: um kit de desenvolvimento de software que ajuda os usuários a desenvolver e testar suas técnicas localmente, e um sistema online que permite que usuários submetam técnicas para que as mesmas sejam automaticamente avaliadas no nosso servidor. Para demonstramos a efetividade do ambiante proposto, modificamos quatro renderizadores e várias técnicas de remoção de ruído — incluindo técnicas recentes baseadas em aprendizado de máquina — e efetuamos uma avaliação utilizando cenas de diferentes renderizadores

Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models

This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm (and the particle filter within) together with the reader. This final implementation is also available as the package pmhtutorial in the CRAN repository. Throughout the tutorial, we provide some intuition as to how the algorithm operates and discuss some solutions to problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian state-space model with synthetic data and a nonlinear stochastic volatility model with real-world data.Comment: 41 pages, 7 figures. In press for Journal of Statistical Software. Source code for R, Python and MATLAB available at: https://github.com/compops/pmh-tutoria