Pixel resolution plenoptic disparity using cost aggregation

We present a hierarchical method for estimating pixel resolution disparity from a raw Plenoptic 2.0 light field capture. Accurate pixel resolution disparity is essential for reconstruction of a high quality conventional image, and also for various applications that depend on disparity, like object segmentation, bokeh etc. Most light field disparity estimation methods in the literature compute disparity at microlens resolution, which is much lower than the resolution of the final reconstructed image. The algorithms that do compute pixel resolution disparity are iterative, making them computationally complex. The proposed method computes disparity hierarchically, in two steps. In the first step, microlens resolution disparity is computed, using which a conventional high resolution image is reconstructed. In the second step, globally smooth and accurate disparity is estimated at a pixel level on the reconstructed image, using the computationally efficient minimum spanning tree based cost aggregation approach. Experimental results demonstrate that the accuracy of the disparity maps generated by our method, in comparison to the Multibaseline and Raytrix algorithms is superior.