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Texture Synthesis and Transfer: Data-Driven Methods in Computational Photography

Explore data-driven methods for texture synthesis and transfer in computational photography, covering concepts such as image analogies, artistic filters, colorization, super-resolution, and graph cuts. Learn from experts in the field and discover the advantages of graph cuts over dynamic programming. Delve into scene completion techniques and understand the intricate process of texture synthesis using innovative algorithms. Enhance your understanding of image processing and manipulation for creative photography applications.

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Texture Synthesis and Transfer: Data-Driven Methods in Computational Photography

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Presentation Transcript

  1. Previous lecture • Texture Synthesis • Texture Transfer = +

  2. Data-driven methods: Texture 2 (Sz 10.5) Cs129 Computational Photography James Hays, Brown, fall 2012 Many slides from Alexei Efros

  3. Image Analogies Aaron Hertzmann1,2 Chuck Jacobs2 Nuria Oliver2 Brian Curless3 David Salesin2,3 1New York University 2Microsoft Research 3University of Washington

  4. Image Analogies A A’ B B’

  5. Blur Filter

  6. Edge Filter

  7. Artistic Filters A A’ B B’

  8. Colorization

  9. Texture-by-numbers A A’ B B’

  10. Super-resolution Input A A’

  11. Super-resolution Result B B’

  12. Graphcut Textures: Image and Video Synthesis Using Graph CutsVivek Kwatra , Arno Schödl , Irfan Essa , Greg Turk and Aaron BobickTo appear in Proc. ACM Transactions on Graphics, SIGGRAPH 2003

  13. Graph cut vs Dynamic Programming • What’s the advantage of a graph cut over dynamic programming here?

  14. Scene Completion

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