Depth image enlargement using an evolutionary approach

• We propose a depth image enlargement approach.
• We exploit a coupled graph that consists of a depth map and a color image.
• We exploit ant colony optimization to reconstruct the high-resolution depth image.

Accurate depth map at high resolution is required in many 3D video display concepts. Given a low-resolution depth map, this paper studies how to enhance its resolution with a registered high-resolution color image. The idea of the proposed approach is that pixels with similar color values and small distances should have similar depth values. Therefore, the known depth values in input depth map can be propagated to estimate the unknown depth values of their neighboring pixels with similar color values and small distances in high-resolution depth map. Different from conventional approaches, the proposed approach utilizes the ant colony optimization (ACO) technique to dispatch artificial ants moving on a coupled graph, which consists of a depth map and a color image. Then these artificial ants propagate the known depth information from the observed low-resolution depth map to its up-sampled counterpart. Experimental results show that the proposed approach achieves high-resolution depth map with more desirable quality than that of several conventional approaches.