Abstract： To solve the problem of insufficient suppression of halo artifacts in the classic methods, the bilateral weights were embedded into the guided filter to propose a new local method of bilateral weighted guided image filtering. The objective function was constructed as bilateral weighted ridge regression model, which could be solved by the iterative execution of bilateral filter. The fast approximation of bilateral filter was adopted to improve computational efficiency. Through the analysis on the filter kernel, it was shown that the more flexible edge-preserving and filtering control were provided by the proposed bilateral weighted guided filtering than traditional methods. The proposed method was extended for the color image filtering. The experiments on two image processing applications of edge-preserving smoothing and portrait skinning were conducted to evaluate the proposed method. The running efficiencies were compared and analyzed. The results show that the outputs produced by bilateral weighted guided filtering have better visual quality compared with those of most classic edge-preserving filtering methods, and the BIQI, IL-NIQE and SSEQ of the proposed method can reach respective 20.082, 47.026 and 53.103.
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