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

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

Introduction Problem Goal Additional Properties Usage 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

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

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

Filter Model Constant-width filter Variable-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

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

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

Result

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?