Learning Iterative Image Reconstruction

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 different 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.

• Type: Conference
• Year: 2001
• Where: IJCAI
• Authors: Sven Behnke
• Keywords: IJCAI 2001, IJCAI 2007, Images, Recurrent Neural Networks, Successful Image Reconstruction