Superpixel-Guided Adaptive Image Smoothing
Hyunjun Eun, Changick Kim · IEEE Signal Processing Letters · 2016
In edge-preserving image smoothing, edge blurriness and structural edge attenuation have been common problems. L0smoothing successfully solves these two problems by adopting L0norm of gradients. However, a weak structural edge diminishing problem still exists because L0penalty first removes small nonzero gradients. In order to address this problem, we propose superpixel-guided adaptive image smoothing by introducing an adaptive parameter into L0smoothing framework. The adaptive smoothing parameter is efficiently computed in a cascade manner. In the first stage, we allocate smoothing parameters to the pixels consisting of details. More importantly, we then exploit similarities between a pixel and its surrounding superpixels for assigning smoothing parameters to the rest pixels. Experimental results demonstrate that our proposed method efficiently preserves structural edges regardless of their scales compared to previous methods.