Convolutional Analysis Operator Learning: Acceleration and Convergence

@article{Chun2020ConvolutionalAO,
  title={Convolutional Analysis Operator Learning: Acceleration and Convergence},
  author={I. Y. Chun and J. Fessler},
  journal={IEEE Transactions on Image Processing},
  year={2020},
  volume={29},
  pages={2108-2122}
}
  • I. Y. Chun, J. Fessler
  • Published 2020
  • Mathematics, Computer Science, Medicine
  • IEEE Transactions on Image Processing
  • Convolutional operator learning is gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called patch-domain approaches that extract and store many overlapping patches across training signals. Due to memory demands, patch-domain methods have limitations when learning kernels from large datasets – particularly with multi-layered structures, e.g., convolutional neural networks – or when applying the learned kernels to high… CONTINUE READING
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