Autor:	Schuler, Christian J., Hirsch, Michael, Harmeling, Stefan, Schölkopf, Bernhard
Rok vydání:	2014
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	We describe a learning-based approach to blind image deconvolution. It uses a deep layered architecture, parts of which are borrowed from recent work on neural network learning, and parts of which incorporate computations that are specific to image deconvolution. The system is trained end-to-end on a set of artificially generated training examples, enabling competitive performance in blind deconvolution, both with respect to quality and runtime.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1406.7444 Zobrazit plný text záznamu View this record from Arxiv