*Article* **Hierarchical Guided-Image-Filtering for E** ffi**cient Stereo Matching**

**Chengtao Zhu 1and Yau-Zen Chang 2,3,\***


Received: 25 June 2019; Accepted: 30 July 2019; Published: 1 August 2019

#### **Featured Application: Potential applications of the work include autonomous navigation, 3D reconstruction, and vision-based object handling.**

**Abstract:** Stereo matching is complicated by the uneven distribution of textures on the image pairs. We address this problem by applying the edge-preserving guided-Image-filtering (GIF) at di fferent resolutions. In contrast to most multi-scale stereo matching algorithms, parameters of the proposed hierarchical GIF model are in an innovative weighted-combination scheme to generate an improved matching cost volume. Our method draws its strength from exploiting texture in various resolution levels and performing an e ffective mixture of the derived parameters. This novel approach advances our recently proposed algorithm, the pervasive guided-image-filtering scheme, by equipping it with hierarchical filtering modules, leading to disparity images with more details. The approach ensures as many di fferent-scale patterns as possible to be involved in the cost aggregation and hence improves matching accuracy. The experimental results show that the proposed scheme achieves the best matching accuracy when compared with six well-recognized cutting-edge algorithms using version 3 of the Middlebury stereo evaluation data sets.

**Keywords:** stereo matching; cost aggregation; image filtering; binocular stereo vision
