  Edge-preserving image smoothing is a fundamental procedure for many computer
vision and graphic applications. There is a tradeoff between the smoothing
quality and the processing speed: the high smoothing quality usually requires a
high computational cost which leads to the low processing speed. In this paper,
we propose a new global optimization based method, named iterative least
squares (ILS), for efficient edge-preserving image smoothing. Our approach can
produce high-quality results but at a much lower computational cost.
Comprehensive experiments demonstrate that the propose method can produce
results with little visible artifacts. Moreover, the computation of ILS can be
highly parallel, which can be easily accelerated through either multi-thread
computing or the GPU hardware. With the acceleration of a GTX 1080 GPU, it is
able to process images of 1080p resolution ($1920\times1080$) at the rate of
20fps for color images and 47fps for gray images. In addition, the ILS is
flexible and can be modified to handle more applications that require different
smoothing properties. Experimental results of several applications show the
effectiveness and efficiency of the proposed method. The code is available at
\url{https://github.com/wliusjtu/Real-time-Image-Smoothing-via-Iterative-Least-Squares}
