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Fast Filtering and Tone Mapping using Importance sampling

This paper discusses the implementation of fast filtering and tone mapping techniques using importance sampling. It explores the conversion of low dynamic range images to high dynamic range images using tone mapping, and highlights the importance of human visual adaptation and glare maps in the process. The paper also presents a filtering method that utilizes importance sampling to efficiently compute filter kernels.

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Fast Filtering and Tone Mapping using Importance sampling

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  1. Fast Filtering and Tone Mapping using Importance sampling Balázs Tóth and László Szirmay-Kalos BUTE Dept. of Control Engineering and Information Technology

  2. Introduction • Low Dynamic Range • RGB 8bit/channel • High Dynamic Range • RGB 32bit/channel • Conversion with Tone Mapping • L final intensity • Yr relative luminance • Yglare additional light scattering • V local adaptation map

  3. Human visual adaptation • High range adaptation • Sunlight, moonlight • The pupil • 2-8mm ~16 times • The cone-rod system • Photopic • Scotopic

  4. Adaptation model • The time course of adaptation

  5. Filtering method • Critical part of implementation • General form of Gaussian filter approximation with finite integral complexity is ο(N2)

  6. Filtering method • Separation of dimensions Two pass 1D convolution with ο(2N) complexity

  7. Filtering method • Importance sampling • Y image value • g() filter kernel tau can be computed and inverted offline

  8. Filtering method • Importance sampling We take samples densely where the filter kernel is large

  9. Tone mapping • Original image

  10. Tone mapping • Luminance

  11. Tone mapping • Global and local averages

  12. Tone mapping • Key value

  13. Tone mapping • Scotopic vision

  14. Tone mapping • Glare map

  15. Tone mapping • Final result

  16. Tone mapping

  17. Tone mapping • Results • HLSL code • NV7800 GPU • Without tone mapping: 380 fps • With tone mapping: 300 fps

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