Learning Iterative Image Reconstruction

  • Sven Behnke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2766)


Successful image reconstruction requires the recognition of a scene and the generation of a clean image of that scene. In this chapter, I show how to use Neural Abstraction Pyramid networks for both analysis and synthesis of images. The networks have a hierarchical architecture which represents images in multiple scales with different degrees of abstraction. The mapping between these representations is mediated by a local recurrent connection structure.

Degraded images are shown to the networks which are trained to reconstruct the originals iteratively. Through iterative reconstruction, partial results provide context information that eliminates ambiguities.

The performance of this approach is demonstrated in this chapter by applying it to four tasks: super-resolution, filling-in of occluded parts, noise removal / contrast enhancement, and reconstruction from sequences of degraded images.


Reconstruction Error Recurrent Neural Network Noise Removal Lateral Projection Occlude Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

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  • Sven Behnke

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