Denoising 1SPP Monte carlo renderings based on human visual perception

Peili Qi; Chunyi Chen

doi:10.1117/12.3032848

5 July 2024 Denoising 1SPP Monte carlo renderings based on human visual perception

Peili Qi, Chunyi Chen

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318422 (2024) https://doi.org/10.1117/12.3032848
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In traditional Monte Carlo (MC) path-tracing denoising approaches, uniform processing across all pixels often overlooks the variable importance of different image regions as perceived by human observers. This study introduces a novel denoising method tailored to 1spp (one sample per pixel) MC renderings, leveraging human visual perception to prioritize computation in visually salient areas. By classifying pixels based on visual saliency, our method efficiently allocates computational resources, enhancing quality in high-saliency regions while reducing unnecessary processing in less noticeable areas. Experimental results validate the effectiveness of our approach, demonstrating improved denoising performance with reduced computational overhead. This saliency-based strategy not only achieves high-quality denoising but also paves the way for more perception-driven approaches in real-time rendering applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Peili Qi and Chunyi Chen "Denoising 1SPP Monte carlo renderings based on human visual perception", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318422 (5 July 2024); https://doi.org/10.1117/12.3032848

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KEYWORDS

Denoising

Visualization

Tunable filters

Image processing

Monte Carlo methods

Light sources and illumination

Image quality

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