反褶积
计算机科学
人工智能
盲反褶积
计算
人工神经网络
深度学习
图像(数学)
集合(抽象数据类型)
图像质量
计算机视觉
模式识别(心理学)
机器学习
算法
程序设计语言
作者
Christian J. Schuler,Michael Hirsch,Stefan Harmeling,Bernhard Schölkopf
标识
DOI:10.1109/tpami.2015.2481418
摘要
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.
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