Edge preserving suppression for depth estimation via comparative variation

Abstract

Most applications in computer vision manage to suppress textures and noise while maintaining meaningful structure based on colour intensity variation, but it is intractable due to texture patterns or error. This study presents an edge-preserving suppression method for depth estimation. The authors formulate a functional energy function based on the relative total intensity and space variation, and they minimise the energy function via iteratively reweighted least squares. Assuming that textural edges most likely correspond to depth discontinuities, they exploit the comparative variations of the colour image to produce a more accurate depth map. The experimental results demonstrate the usefulness of the proposed approach, and show that texture patterns are suppressed while meaningful edges are preserved. According to the results of the depth acquisition methods, the proposed depth estimation methods generate the accurate and robust results.