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Gradient Outlier Removal for Gradient‐Domain Path Tracing
Author(s) -
Ha Saerom,
Oh Sojin,
Back Jonghee,
Yoon SungEui,
Moon Bochang
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13634
Subject(s) - outlier , path (computing) , pixel , computer science , tracing , artificial intelligence , anomaly detection , image gradient , domain (mathematical analysis) , ray tracing (physics) , algorithm , computer vision , image (mathematics) , mathematics , image processing , optics , physics , mathematical analysis , edge detection , programming language , operating system
We present a new outlier removal technique for a gradient‐domain path tracing (G‐PT) that computes image gradients as well as colors. Our approach rejects gradient outliers whose estimated errors are much higher than those of the other gradients for improving reconstruction quality for the G‐PT. We formulate our outlier removal problem as a least trimmed squares optimization, which employs only a subset of gradients so that a final image can be reconstructed without including the gradient outliers. In addition, we design this outlier removal process so that the chosen subset of gradients maintains connectivity through gradients between pixels, preventing pixels from being isolated. Lastly, the optimal number of inlier gradients is estimated to minimize our reconstruction error. We have demonstrated that our reconstruction with robustly rejecting gradient outliers produces visually and numerically improved results, compared to the previous screened Poisson reconstruction that uses all the gradients.