Recovering depth from images using adaptive depth from focus

Depth estimation from a sequence of images is a challenging problem in computer vision research. One of the well-known solutions is the depth from focus. However, the drawbacks of this method are the tradeoff between spatial resolution and robustness, and failure in textureless regions. In this paper, a novel approach of depth from focus with multiple images is proposed to improve the two shortcomings. By employing the mean shift segmentation before the step of building Markov random field, the result of segmentation serves as adaptive window for DFF. The edges of the recovered depth map are guaranteed to align with the edges of the original image. After the initial estimation of depth, the hierarchical Markov random field is generated to expand the area to extract depth information according to the structure of the scene. In this way, the experiments show that depth can extract from the textureless regions to some extent.