Propagated guided image filtering for edge-preserving smoothing

This paper proposes an edge-preserving smoothing filtering algorithm based on guided image filter (GF). GF is a well-known edge-preserving smoothing filter, but is ineffective in certain cases. The proposed GF enhancement provides a better solution for various noise levels associated with image degradation. In addition, halo artifacts, the main drawback of GF, are well suppressed using the proposed method. In our proposal, linear GF coefficients are updated sequentially in the spatial domain by using a new cost function, whose solution is a weighted average of the neighboring coefficients. The weights are determined differently depending on whether the pixels belong to the edge region, and become zero when a neighborhood pixel is located within a region separated from the center pixel. This propagation procedure is executed twice (from upper-left to lower-right, and vice versa) to obtain noise-free edges. Finally, the filtering output is computed using the updated coefficient values. The experimental results indicate that the proposed algorithm preserves edges better than the existing algorithms, while reducing halo artifacts even in highly noisy images. In addition, the algorithm is less sensitive to user parameters compared to GF and other modified GF algorithms.