Applications Of Digital Image Filtering In Stereo Matching

One of the most important technology in many3D applications is acquiring the depth of3-dimensional scenes quickly and accurately. With the development of3D technology, new requirements are raised by many new applications such as3D surface modeling,3D television and argument reality. These applications usually require more accurate dense depth map on one hand, and more efficient depth generation algorithms on the other hand.3DTV integrates many technologies such as3D computer animation, real scene depth acquisition and virtual-real fusion. The demand of flexible viewing experience requires more advanced depth generation algorithms. Based on the classic3DTV, our research team proposed a novel concept named "natural3DTV" which aims to reproduce the real3D scenes using a more comfortable way. And the author also proposed using "video+depth+enhanced information" to represent the3D scenes. Natural3DTV system captures the actual scene, generates the corresponding depth, and synthesizes multiple virtual view images using the depth image-based rendering (DIBR) technology. And the quality of virtual views generated by DIBR heavily depends on the accuracy of depth maps. Some real-time applications also ask for more efficient algorithms.Stereo matching is one of the widely used depth acquisition methods. It can generate dense depth maps and requiring no active projection instruments. But the problem itself is ill-posed and must be solved under certain assumptions or constraints. Many algorithms were proposed and the technology has been well developed. These algorithms can be mainly categorized into two classes:global methods that rely on the global optimization and local algorithms emphasize on local cost aggregation. Based on these existing methods, the author made a deep study of stereo matching algorithms. The main contributions of this doctoral thesis are as follows:1. Based on existing local stereo matching algorithms, the author successfully integrates the digital image filtering technology and proposed a novel local stereo matching framework based on image filtering. The framework uses image filtering models to carry out two main steps in local algorithms:matching cost aggregation and disparity post-processing. This allows us to improve the accuracy and efficiency of stereo matching algorithms by applying efficient image filters.2. Within the novel local stereo matching framework, the author proposed a new adaptive guided image filtering method directed by the novel concept of "two-level local adapta-tion". The new cost filtering method improved the accuracy of disparity maps compared to methods using the original guided image filter. The author also designed the work-efficient "parallel integral image" parallel algorithm and implemented the whole algorithm on the NVIDIA CUDA parallel computing platform. The speed of the whole matching algorithm is greatly improved.3. The author proposed a novel full-image guided filtering method based on orthogonal geodesic distance weight. This filtering method can utilize as many supporting pixel-s as possible and ensures strong edges being kept. By applying it to cost filtering and post-processing, both present good performance. According the the property of the or-thogonal geodesic distance, the author also designed a fast implementation to reduce the computational amount.4. To deal with the key problem of disparity post-processing, the author also proposed two post-processing filtering methods. One method uses novel hierarchical joint bilateral fil-tering to process the disparity map directly, the other method operates on the transformed cost volume by applying full-image guided filtering. Both methods can deal with occluded regions and mismatched pixels. The later method performs well even at large low-texture regions.