Synthesis-analysis deconvolutional network for compressed sensing

@article{Liu2017SynthesisanalysisDN,
  title={Synthesis-analysis deconvolutional network for compressed sensing},
  author={Qiegen Liu and Henry Leung},
  journal={2017 IEEE International Conference on Image Processing (ICIP)},
  year={2017},
  pages={1940-1944}
}
Synthesis learning and analysis learning, with sparse coding (SC) and Markov random fields (MRFs) as two representative types of models, are two complementary tools to describe the image manifolds. SC has strengths in representing the regular features/explicit visual manifolds while its effectiveness depends on the training dataset. While MRFs have great… CONTINUE READING