ALRe: Outlier Detection for Guided Refinement
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Guided refinement is a popular procedure of various image post-processing applications. It produces output image based on input and guided images. Input images are usually flawed estimates containing kinds of noises and outliers, which undermine the edge consistency between input and guided images. As improvements, they are refined into output images with similar intensities of input images and consistent edges of guided images. However, outliers are usually untraceable and simply treated as zero-mean noises, limiting the quality of such refinement. In this paper, we propose a general outlier detection method for guided refinement. We assume local linear relationship between output and guided images to express the expected edge consistency, based on which, the outlier likelihoods of input pixels are measured. The metric is termed as ALRe (anchored linear residual) since it is essentially the residual of local linear regression with an equality constraint exerted on the measured pixel. Valuable features of the ALRe are discussed. Its effectiveness is proven by applications and experiment.
KeywordsAnchored linear residual Outlier detection Guided refinement Local linear assumption Linear regression
This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1312800 and in part by the National Natural Science Foundation of China under Grant U1909206.
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