Depth from defocus using superpixel‐based affinity model and cellular automata

Abstract

Depth from defocus (DFD) technique calculates the blur amount in images considering that the depth and defocus blur are related to each other. Existing DFD methods generally compute the blur at edge locations and solve an optimisation problem to propagate the blur from edges to all image pixels. Solving the pixel-based optimisation problem is time-consuming, posing the performance bottleneck. Moreover, the generated depth maps are not consistent in textured areas and the blur estimation may be incorrect in the regions with soft shadows. We address these problems by proposing a superpixelbased blur estimation method. Experimental results show that the proposed method is not only faster than pixel-based blur estimation, but also can improve depth data in textured regions and soft shadows.