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Energy Preserving Non-linear Filters

Energy Preserving Non-linear Filters. Presented by Wei-Yin Chen (R94943040). Outline. Introduction Problem and Goal Source of Noise Filter Model Algorithm and Application Monte Carlo RADIANCE Result and Conclusion. Introduction. Problem Noise caused by inadequate sampling Goal

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Energy Preserving Non-linear Filters

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  1. Energy Preserving Non-linear Filters Presented by Wei-Yin Chen (R94943040)

  2. Outline • Introduction • Problem and Goal • Source of Noise • Filter Model • Algorithm and Application • Monte Carlo • RADIANCE • Result and Conclusion

  3. Introduction • Problem • Noise caused by inadequate sampling • Goal • Enhance image quality without more samples • Additional Properties • Preserve energy • Doesn’t blur details • Usage • Filter before tone mapping

  4. Source of Noise • “Small probability” area in Monte-Carlo method • Not noise actually • This happens at a region, not at a pixel • Average the “noise” in a larger region

  5. Required sample • Define noise • Pixels not converging within range D (typically 13) after tone map • Huge samples are required in the worst case • >1e4 samples for 1e-4 accuracy • Most regions are smooth • Good average case • Target on the noisy regions

  6. Filter Model • Constant-width filter • Inherently preserve energy • Variable-width filter • Not energy preserving on the boundary • Region of support • Variable-width filter with energy preserving • Source oriented perspective • Region of influence

  7. Algorithm for Monte Carlo rendering • Pre-render a small image (100x100x16) • Find a visual threshold Ltvis = Laverage/128 (1 after tone map) • Find a threshold of sample density that most pixels converge within D • Render with the sampling density at full resolution • For unconverged pixels • Distribute Lexcess=Lu-Ln (average of converged neighbors) to a region, region area = Lexcess/Ltvis • Affected radiance <= 1

  8. Co-operation with RADIANCE • RADIANCE • A rendering system • Super-sampled • StDev unknown for the filter • Work-around • Regard extreme values as unconverged pixels

  9. Result

  10. Conclusion and Comments • Effective in removing noise • Still blur the caustic area • Increasing samples in the noisy region might be better • What if the RADIANCE is not super-sampled?

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