Embedding Bilateral Filter in Least Squares for Efficient Edge-Preserving Image Smoothing

Abstract

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 the local ones. However, local methods usually run much faster than the global ones. In this paper, we propose a new global method that embeds the bilateral filter (BLF) in the least squares (LS) 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 BLF and the LS model, it runs much faster. In addition, we show the flexibility of our method which can be easily extended by replacing the BLF 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.