Distributed gradient-domain processing of planar and spherical images

  • Authors:
  • Michael Kazhdan;Dinoj Surendran;Hugues Hoppe

  • Affiliations:
  • Johns Hopkins University, Baltimore, MD;Microsoft Research, Redmond, WA;Microsoft Research, Redmond, WA

  • Venue:
  • ACM Transactions on Graphics (TOG)
  • Year:
  • 2010

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Abstract

Gradient-domain processing is widely used to edit and combine images. In this article 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, dynamic-range 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.