Neural Denoising for Path Tracing of Medical Volumetric Data
| Autor: | Marc Stamminger, Nikolai Hofmann, Klaus Engel, Jana Martschinke |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Pixel
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Stability (learning theory) 020207 software engineering Volume rendering Image processing 02 engineering and technology Iterative reconstruction Computer Graphics and Computer-Aided Design Sample (graphics) 030218 nuclear medicine & medical imaging Computer Science Applications Rendering (computer graphics) 03 medical and health sciences 0302 clinical medicine Path tracing 0202 electrical engineering electronic engineering information engineering Computer vision Artificial intelligence business ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Proceedings of the ACM on Computer Graphics and Interactive Techniques. 3:1-18 |
| ISSN: | 2577-6193 |
| DOI: | 10.1145/3406181 |
| Popis: | In this paper, we transfer machine learning techniques previously applied to denoising surface-only Monte Carlo renderings to path-traced visualizations of medical volumetric data. In the domain of medical imaging, path-traced videos turned out to be an efficient means to visualize and understand internal structures, in particular for less experienced viewers such as students or patients. However, the computational demands for the rendering of high-quality path-traced videos are very high due to the large number of samples necessary for each pixel. To accelerate the process, we present a learning-based technique for denoising path-traced videos of volumetric data by increasing the sample count per pixel; both through spatial (integrating neighboring samples) and temporal filtering (reusing samples over time). Our approach uses a set of additional features and a loss function both specifically designed for the volumetric case. Furthermore, we present a novel network architecture tailored for our purpose, and introduce reprojection of samples to improve temporal stability and reuse samples over frames. As a result, we achieve good image quality even from severely undersampled input images, as visible in the teaser image. |
| Databáze: | OpenAIRE |
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