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Distributed gradient-domain processing of planar and spherical images

Distributed gradient-domain processing of planar and spherical images
Michael Kazhdan, Dinoj Surendran, Hugues Hoppe.
ACM Trans. Graphics, 29(2), 14, 2010. (Presented at SIGGRAPH 2010.)
Spherical gradient-domain processing on a Terapixel sky.
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, 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.
Hindsights:

See the resulting seamless terapixel night sky in WorldWide Telescope.

In this project, the image data had to be parameterized using the “TOAST” spherical map. As explained in the paper, the resulting gradient-domain solution was therefore inexact. Our subsequent paper Metric-aware processing of spherical imagery shows that when working with the (commonly used) equirectangular parameterization, one can adaptively discretize the domain to achieve an efficient, exact solution over the sphere.

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