Distributed gradient-domain processing of planar and spherical images
(From Hugues Hoppe's publications)
In review.
paper supplemental proof supplemental images
Abstract: Gradient-domain processing is widely used to edit and combine images. In this paper we extend the framework in two directions. First, we adapt the gradient-domain approach to operate on a spherical domain, to enable operations such as seamless stitching, dynamicrange compression, and gradient-based sharpening over spherical imagery. An efficient streaming computation is obtained using a new spherical parameterization with bounded distortion and localized boundary constraints. Second, we design a distributed solver to efficiently process large planar or spherical images. The solver partitions images into bands, streams through these bands in parallel within a networked cluster, and schedules computation to hide the necessary synchronization latency. We demonstrate our contributions on several datasets including the Digitized Sky Survey, a terapixel spherical scan of the night sky.
Hindsight: No hindsights yet.