Adaptive patched L0 gradient minimisation model applied on image smoothing
Kang Ni, Yiquan Wu · IET Image Processing · 2018
L0 gradient minimisation model, one of edge‐aware image smoothing method, also suffers from the stair‐casing effect and images with strong textures cannot be smoothed effectively and weak edges or structures will be smoothed overly. The authors propose a method to overcome these drawbacks above. To begin with, the image is subjected to non‐subsampled shearlet transform to obtain high‐frequency component, and combine all high‐frequency component by maximum local energy rules to obtain the high‐frequency decomposition image, afterwards, introducing the data term associated with high‐frequency decomposition image to keep the similarity of edge and structure between the input and smoothed image. Secondly, the patched L0 gradient minimisation model is presented for improving the description of local information, since different size of the patches has the different texture, exploiting the coefficient of variation to define the size of patch. Finally, defining the adaptive smoothing coefficient based on the gradient to make sure that the smoothing effect of the patch is optimal. The proposed model is applied to image smoothing with desirable results successfully, and the comparisons with other state‐of‐the‐art edge‐preserving image smoothing algorithms demonstrate the great performances of edge‐preserving and texture smoothing.