An improved gradient-based dense stereo correspondence algorithm using guided filter

This paper presents an improved gradient-based real-time stereo correspondence algorithm using guided filter. Color intensity value is sensitive to radiometric distortions including exposure differences and illumination differences, thus the error correspondence rates of these methods are high. deMaeztu et al. [1] proposed to measure the similarity between pixels using the gradient value instead of color intensity. The method has better robustness to radiometric distortions than intensity-based local methods, but the running time is so long that it is not suitable for real-time applications, because the adaptive support weight of neighbor pixels depends on bilateral filter. Guided filter has edge-preserving character as bilateral filter, but runs faster than it, we use guided filter as adaptive support weight instead of bilateral filter of the neighbor pixels in a finite squared support window. The experimental results demonstrate that the improved algorithm performs much better compared with gradient-based method and other local methods, whether in accuracy or efficiency, according to the widely-used Middlebury stereo benchmarks, and the robustness to radiometric distortions of ours is better also.