Adaptive sample map for Monte Carlo ray tracing

Jun Teng; Lixin Luo; Zhibo Chen

doi:10.1117/12.863185

5 August 2010 Adaptive sample map for Monte Carlo ray tracing

Jun Teng, Lixin Luo, Zhibo Chen

Proceedings Volume 7744, Visual Communications and Image Processing 2010; 774430 (2010) https://doi.org/10.1117/12.863185
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China

Abstract

Monte Carlo ray tracing algorithm is widely used by production quality renderers to generate synthesized images in films and TV programs. Noise artifact exists in synthetic images generated by Monte Carlo ray tracing methods. In this paper, a novel noise artifact detection and noise level representation method is proposed. We first apply discrete wavelet transform (DWT) on a synthetic image; the high frequency sub-bands of the DWT result encode the noise information. The sub-bands coefficients are then combined to generate a noise level description of the synthetic image, which is called noise map in the paper. This noise map is then subdivided into blocks for robust noise level metric calculation. Increasing the samples per pixel in Monte Carlo ray tracer can reduce the noise of a synthetic image to visually unnoticeable level. A noise-to-sample number mapping algorithm is thus performed on each block of the noise map, higher noise value is mapped to larger sample number, and lower noise value is mapped to smaller sample number, the result of mapping is called sample map. Each pixel in a sample map can be used by Monte Carlo ray tracer to reduce the noise level in the corresponding block of pixels in a synthetic image. However, this block based scheme produces blocky artifact as appeared in video and image compression algorithms. We use Gaussian filter to smooth the sample map, the result is adaptive sample map (ASP). ASP serves two purposes in rendering process; its statistics information can be used as noise level metric in synthetic image, and it can also be used by a Monte Carlo ray tracer to refine the synthetic image adaptively in order to reduce the noise to unnoticeable level but with less rendering time than the brute force method.

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Jun Teng, Lixin Luo, and Zhibo Chen "Adaptive sample map for Monte Carlo ray tracing", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 774430 (5 August 2010); https://doi.org/10.1117/12.863185

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KEYWORDS

Monte Carlo methods

Discrete wavelet transforms

Light sources and illumination

Ray tracing

Image quality

Visualization

Spherical lenses

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