  Edge-preserving smoothing is a fundamental procedure for many computer vision
and graphic applications. This can be achieved with either local methods or
global methods. In most cases, global methods can yield superior performance
over local ones. However, local methods usually run much faster than global
ones. In this paper, we propose a new global method that embeds the bilateral
filter in the least squares model for efficient edge-preserving smoothing. The
proposed method can show comparable performance with the state-of-the-art
global method. Meanwhile, since the proposed method can take advantages of the
efficiency of the bilateral filter and least squares model, it runs much
faster. In addition, we show the flexibility of our method which can be easily
extended by replacing the bilateral filter with its variants. They can be
further modified to handle more applications. We validate the effectiveness and
efficiency of the proposed method through comprehensive experiments in a range
of applications.
