Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid

• Author: Sven Behnke

 
• International Journal of Computational Intelligence and Applications, Special Issue on Neural Networks at IJCAI-2001, vol. 1, no. 4, pp. 427-438, 2001.

 
• Abstract:
Successful image reconstruction requires the recognition of a scene and the generation of a clean image of that scene. We propose to use recurrent neural networks for both analysis and synthesis.
The networks have a hierarchical architecture that represents images in multiple scales with dif-ferent degrees of abstraction. The mapping between these representations is mediated by a local connection structure.
We supply the networks with degraded images and train them to reconstruct the originals iteratively. This iterative reconstruction makes it possible to use partial results as context information to resolve ambiguities.
We demonstrate the power of the approach using three examples: superresolution, fill-in of occluded parts, and noise removal / contrast enhancement. We also reconstruct images from sequences of degraded images.
 
• Keywords: Neural Abstraction Pyramid, Image Reconstruction, Backpropagation Through Time, Superresolution, Occlusion, Contrast Enhancement

 
• Full paper: ijcia01.pdf