Stereo Matching with Bit-Plane Slicing and Disparity Fusion

This paper presents a new stereo matching technique based on image bit-plane slicing and fusion. Given an 8-bit image, it can be separated to multiple images with 1-bit representation by bit-plane slicing. Instead of performing stereo matching on the original image pair, bit-plane slices are used to find stereo correspondences, followed by image fusion for the final disparity map. The main advantage in our method is the extremely low data access requirement, which is an important issue for high dynamic range (HDR) image applications or stereo matching implementation on embedded systems. The proposed method can adapt to the existing stereo matching techniques such as local block matching (SAD), semi-global block matching, probabilistic correspondence matching (SSMP), and global optimization (belief propagation). In the fusion stage, we investigate wavelet (in the transform domain) and PCA (in the spatial domain) methods. Experiments are carried out on Middlebury datasets for different stereo matching techniques with full bit-rate matching. The results demonstrate that the bad pixel rate can be reduced by our bit-plane matching and disparity fusion framework.