Adaptive side window joint bilateral filter

Edge-preserving image smoothing is a fundamental step for many computer vision problems, and so far, countless algorithms have been proposed. Among these algorithms, bilateral filtering and its extensions are widely used in image preprocessing. However, several difficulties are hindering its further development. First, the phenomenon of halo artifact occurs along the edges. Second, most of the existing algorithms work only with a fixed filtering kernel and cannot accurately distinguish the edges and textures which leads to inappropriate filtering. To address these issues, we present a novel edge-preserving image smoothing via adaptive side window joint bilateral filtering. As a local optimized-based algorithm, different from the traditional filtering, the position of the target pixel in the filtering kernel is changed from the center to the optimal edge and the filtering kernel size of each pixel is effectively estimated. Combined side window filtering with the joint bilateral filter, the capability of texture removal and edge preservation is improved and the halo artifacts are alleviated. Experimental results show that the proposed method outperforms existing state-of-the-arts in removing the texture information while preserving the main image content.

Automated summary

This study presents a novel edge-preserving image smoothing algorithm using adaptive side window joint bilateral filtering. By optimizing the position and size of the filtering kernel, it effectively addresses issues such as halo artifact and texture removal.