Large aperture focus stacking with max-gradient flow by anchored rolling filtering.

Focus stacking is a computational technique to extend the depth of field through combining multiple images taken at various focus distances. However, in the large aperture case, there are always defects caused by the large blur scale, which, to the best of our knowledge, has not been well studied. In our work, we propose a max-gradient flow-based method to reduce artifacts and obtain a high-quality all-in-focus image by anchored rolling filtering. First, we define a max-gradient flow to describe the gradient propagation in the stack. The points are divided into trivial and source points with this flow. The source points are extracted as true edge points and are utilized as anchors to refine the depth map and the composited all-in-focus image iteratively. The experiments show that our method can effectively suppress the incorrect depth estimations and give a high-quality all-in-focus image.

Iteratively reconstructing 4D light fields from focal stacks.

Acquiring and representing the 4D space of rays in the world (the light field) is important for many computer vision and graphics applications. In this paper, we propose an iterative method to acquire the 4D light field from a focal stack. First, a discrete refocusing equation is derived from integral imaging principles. With this equation, a linear projection system is formulated to model the focal stack imaging process. Then we reconstruct the 4D light field from the focal stack through solving the inverse problem with a filtering-based iterative method. The experimental results show that our approach is effective and outperforms state-of-the-art methods in reconstruction accuracy, reduced sampling, and occluded boundaries.

Depth estimation for speckle projection system using progressive reliable points growing matching.

In this paper, we propose a progressive reliable points growing matching scheme to estimate the depth from the speckle projection image. First a self-adapting binarization is introduced to reduce the influence of inconsistent intensity. Then we apply local window-based correlation matching to get the initial disparity map. After the initialization, we formulate a progressive updating scheme to update the disparity estimation. There are two main steps in each round of updation. At first new reliable points are progressively selected based on three aspects of criterion including matching degree, confidence, and left-right consistency; then prediction-based growing matching is adopted to recalculate the disparity map from the reliable points. Finally, the more accurate depth map can be obtained by subpixel interpolation and transformation. The experimental results well demonstrate the effectiveness and low computational cost of our scheme.