Subspace based disparity estimation for plenoptic cameras

Abstract

We present a subspace based disparity estimation technique for plenoptic 2.0 lightfield cameras. The raw lightfield image contains a micro-image for every lens in the micro-lens array. The disparity of a scene point is typically estimated using multi-baseline approach. The multi-baseline approach necessitates that a focussed copy of a patch is present in at least one of the neighboring micro-images. This requirement limits the range over which the disparity can be reliably estimated. We propose a subspace based technique for disparity estimation wherein a subspace for every disparity is learnt separately, and the learnt subspaces are subsequently used for estimating the disparity of any micro-image. We estimate the disparities for the images captured using Raytrix R11 camera and compare the results with (a) estimates obtained from the multi-baseline approach, and (b) manufacturer provided disparity maps. Comparisons show that the disparity maps estimated by the proposed technique are superior. In addition, the proposed technique allows for extending the range over which the disparity can be estimated.