Robust weighted least squares for guided depth upsampling

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Conference Proceeding
2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 559 - 563
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In this paper, we propose a new guided depth upsampling method denoted as Robust Weighted Least Squares (RWLS). Our work is inspired by the connection between theWeighted Least Squares (WLS) and the Auto Regressive (AR) model. By adopting a new robust penalty function to model the smoothness of the proposed model, we show that the proposed method performs much better in preserving sharp depth discontinuities than previous work. Through both mathematical analysis and experimental results, we show that our method has promising performance on handling the inconsistency between the guidance image and the depth map in both preserving sharp depth discontinuities and suppressing the texture copy artifacts.
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