Learning Deep Gradient Descent Optimization for Image Deconvolution

@article{Gong2020LearningDG,
  title={Learning Deep Gradient Descent Optimization for Image Deconvolution},
  author={Dong Gong and Zhen Zhang and Qinfeng Shi and Anton van den Hengel and Chunhua Shen and Yanning Zhang},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2020},
  volume={31},
  pages={5468-5482}
}
As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature of the inverse problem. [...] Key Method We propose a Recurrent Gradient Descent Network (RGDN) by systematically incorporating deep neural networks into a fully parameterized gradient descent scheme. A parameter-free update unit is used to generate updates from the current estimates, based on a convolutional neural network.Expand
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