Edge-preserving image smoothing with local constraints on gradient and intensity
Pan Shao, Shouhong Ding, Lizhuang Ma · 2015
We present a new edge-preserving image smoothing approach by incorporating local features into a holistic optimization framework. Our method embodies a gradient constraint to enforce detail eliminating and an intensity constraint to achieve shape maintaining. The gradients of high-contrast details are suppressed to a lower magnitude, subsequent to which structural edges can be located. The intensities of a small region are regulated to resemble the initial fabric, which facilitates further detail capture. Experimental results indicate that the proposed algorithm, availed by a sparse gradient counting mechanism, can properly smooth non-edge regions even when textures and structures are similar in scale. The effectiveness of our approach is demonstrated in the context of detail manipulation, edge detection, and image abstraction.