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Fractional Differential Filter for Stereo Matching

It is known that weak texture region have become the major barriers to the development of stereo matching. The lack of appropriate feature will make it difficult to find another corresponding pixel also have no feature. The fractional differential-based approach for image filtering have the capability of nonlinearly enhancing complex texture details obvious better than by traditional integral-based algorithms. In this article, the cost aggregation consists of two pieces: the weighted guided image filtering for color-scale; the image after fractional differential filtering as the guidance image used to guided image filtering (GIF) for grayscale. The aggregated values of two scales will represent the edge and weak texture area, respectively. Finally, a disparity refinement measure based on fast weighted median filtering is applied in this paper too. Performance evaluation on Middlebury data sets shows that the proposed algorithm can obtain high-quality, especially in weak texture region. It's an attractive stereo matching solution in practice for both speed and accuracy.