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Deep Convolutional Framelets: A General Deep Learning for Inverse Problems. (arXiv:1707.00372v1 [stat.ML])
来源于:arXiv
Recently, deep learning approaches have achieved significant performance
improvement in various imaging problems. However, it is still unclear why these
deep learning architectures work. Moreover, the link between the deep learning
and the classical signal processing approaches such as wavelet, non-local
processing, compressed sensing, etc, is still not well understood, which often
makes signal processors in deep troubles. To address these issues, here we show
that the long-searched-for missing link is the convolutional framelets for
representing a signal by convolving local and non-local bases. The
convolutional framelets was originally developed to generalize the recent
theory of low-rank Hankel matrix approaches, and this paper significantly
extends the idea to derive a deep neural network using multi-layer
convolutional framelets with perfect reconstruction (PR) under rectified linear
unit (ReLU). Our analysis also shows that the popular deep network components
such as residual blo 查看全文>>