Image Smoothing by Using First and Second Order Region Statistics
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Recent years have witnessed the emergence of new image smoothing techniques which have provided new insights and raised new questions about the nature of this well-studied prob- lem. Specifically, these models separate a given image into its structure and texture layers by utilizing non-gradient based definitions for edges or special measures that distinguish edges from oscillations. In this thesis, we propose an alternative yet simple image smoothing approach which depends on 1st and 2nd order feature statistics of image regions. The use of these region statistics as a patch descriptor allows us to implicitly capture local structure and texture information and makes our approach particularly effective for structure extraction from texture. Our experimental results have shown that the proposed approach leads to better image decomposition as compared to the state-of-the-art methods and preserves prominent edges and shading well.