Image Simplification by Robust Estimator Based Reconstruction Filter

In this contribution, we discuss a robust estimator based on image reconstruction technique for image filtering and simplification purposes. Instead of using the least-squares estimators that the measurement error is independently random and distributed as a normal distribution, a Lorentzian distribution based estimator is employed to fit a model function to the input image within the local windows. The estimator weights the outliers of the measurement inversely with respect to the their deviations unlike the least-squares that magnifies exponentially. We adapted the robust estimator to simplify image by treating image noise and texture as measurement deviations. The results prove that the simplification filters have potential of becoming popular image processing tools.