Improved Real-time Image Smoothing with Weak Structures Preserved and
  High-contrast Details Removed
Shengchun Wang Wencheng Wang Fei Hou
Image smoothing is by reducing pixel-wise gradients to smooth out details. As
existing methods always rely on gradients to determine smoothing manners, it is
difficult to distinguish structures and details to handle distinctively due to
the overlapped ranges of gradients for structures and details. Thus, it is
still challenging to achieve high-quality results, especially on preserving
weak structures and removing high-contrast details. In this paper, we address
this challenge by improving the real-time optimization-based method via
iterative least squares (called ILS). We observe that 1) ILS uses gradients as
the independent variable in its penalty function for determining smoothing
manners, and 2) the framework of ILS can still work for image smoothing when we
use some values instead of gradients in the penalty function. Thus,
corresponding to the properties of pixels on structures or not, we compute some
values to use in the penalty function to determine smoothing manners, and so we
can handle structures and details distinctively, no matter whether their
gradients are high or low. As a result, we can conveniently remove
high-contrast details while preserving weak structures. Moreover, such values
can be adjusted to accelerate optimization computation, so that we can use
fewer iterations than the original ILS method for efficiency. This also reduces
the changes onto structures to help structure preservation. Experimental
results show our advantages over existing methods on efficiency and quality.