Image analogies

  • Authors:
  • Aaron Hertzmann;Charles E. Jacobs;Nuria Oliver;Brian Curless;David H. Salesin

  • Affiliations:
  • New York University and Microsoft Research;Microsoft Research;Microsoft Research;University of Washington;Microsoft Research and University of Washington

  • Venue:
  • Proceedings of the 28th annual conference on Computer graphics and interactive techniques
  • Year:
  • 2001

Quantified Score

Hi-index 0.02

Visualization

Abstract

This paper describes a new framework for processing images by example, called “image analogies.” The framework involves two stages: a design phase, in which a pair of images, with one image purported to be a “filtered” version of the other, is presented as “training data”; and an application phase, in which the learned filter is applied to some new target image in order to create an “analogous” filtered result. Image analogies are based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis. By choosing different types of source image pairs as input, the framework supports a wide variety of “image filter” effects, including traditional image filters, such as blurring or embossing; improved texture synthesis, in which some textures are synthesized with higher quality than by previous approaches; super-resolution, in which a higher-resolution image is inferred from a low-resolution source; texture transfer, in which images are “texturized” with some arbitrary source texture; artistic filters, in which various drawing and painting styles are synthesized based on scanned real-world examples; and texture-by-numbers, in which realistic scenes, composed of a variety of textures, are created using a simple painting interface.