Depth map estimation using census transform for light field cameras

Abstract

Depth estimation for the lense-array type cameras is a challenging problem because of sensor noise and radiometric distortion which is a global brightness change between sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against the sensor noise and the radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial map obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. We refine an initial map by the optimization which uses the assumption that the variations of the depth values in the depth map and of the pixel values in the texture-less objects are similar. Experiments show that our method outperforms the conventional methods.