Accelerating Denoising at the Speed of Light

Introduction

In computer graphics, real-time ray tracing has become widely adopted for generating high-quality visuals in applications like gaming and interactive simulations. A significant challenge in ray tracing is that using a low number of samples per pixel often results in noisy images, limiting their practical use. Achieving high-quality images typically requires ray tracing with a large number of samples per pixel, which demands substantial computational power and makes real-time generation difficult. Consequently, there is a growing need for effective noise reduction techniques for images rendered with fewer samples per pixel. Efficient denoising can produce high-quality images that preserve scene realism while optimizing computational resources.

Background and Problem Setup

Background

While applications such as gaming typically render high-resolution images (e.g., 1080p, 4K), recent advancements in fields like robotics have created a demand for extremely fast, real-time rendering of low-resolution images \cite{7019765}, \cite{8860966}. This project specifically addresses this challenge, focusing on developing high-quality and efficient denoising techniques for low-resolution ray-traced images.

Problem Definition

Given a 64x64 image rendered with one sample per pixel, along with other features that can be obtained using similar computational resources, we propose a denoising framework capable of producing a 64x64 output image that closely matches the quality of a ground-truth image rendered with 512 samples per pixel. Our framework is evaluated primarily based on two key criteria:

Quality: The generated image should closely replicate the realism and quality of the ground-truth image. Quality is assessed using Peak Signal-to-Noise Ratio (PSNR). Performance: The system should be computationally efficient. Performance is evaluated by the number of frames it can denoise per second, serving as a secondary metric.

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