18,754 research outputs found
Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing
By integrating certain optimization solvers with deep neural network, deep
unfolding network (DUN) has attracted much attention in recent years for image
compressed sensing (CS). However, there still exist several issues in existing
DUNs: 1) For each iteration, a simple stacked convolutional network is usually
adopted, which apparently limits the expressiveness of these models. 2) Once
the training is completed, most hyperparameters of existing DUNs are fixed for
any input content, which significantly weakens their adaptability. In this
paper, by unfolding the Fast Iterative Shrinkage-Thresholding Algorithm
(FISTA), a novel fast hierarchical DUN, dubbed FHDUN, is proposed for image
compressed sensing, in which a well-designed hierarchical unfolding
architecture is developed to cooperatively explore richer contextual prior
information in multi-scale spaces. To further enhance the adaptability, series
of hyperparametric generation networks are developed in our framework to
dynamically produce the corresponding optimal hyperparameters according to the
input content. Furthermore, due to the accelerated policy in FISTA, the newly
embedded acceleration module makes the proposed FHDUN save more than 50% of the
iterative loops against recent DUNs. Extensive CS experiments manifest that the
proposed FHDUN outperforms existing state-of-the-art CS methods, while
maintaining fewer iterations.Comment: Accepted by ACM MM 202
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