Improved Image Quilting Jeremy Long David Mould

Introduction   Goal: improve “ minimum error boundary cut ”

Figure 1: Image Quilting (left) and Improved Image Quilting (right).

Method   The non-scalar distance metric   The partial non-scalar distance metric

The conventional cumulative distance metric (left) and the partial non-scalar metric (right) using the same set of 28 endpoints in the same weighted graph.

Visualizations of a graph using the cumulative distance metric (left) and the partial non-scalar distance metric (right).

The partial non-scalar distance metric Algorithm partialNonscalarCompare(Path p1, Path p2): path { if (p1.maxEdgeCost == p2.maxEdgeCost) { return the path with the lower total distance; } else { return the path with the lower maximum edge cost; }

  Uncut patch (left) and the same patch after the minimum error boundary cut generated with the conventional distance metric (center) and the non-scalar distance metric (right).

Result Per pixel error profile along the minimum error boundary cut (left) and the partial non-scalar boundary cut (right).

  Input texture (left), image quilting (center) and improved image quilting (right). Both outputs were generated with a patch size of 32 x 32