Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing
Wenxue Cui, Shaohui Liu, Debin Zhao

TL;DR
The paper introduces FHDUN, a fast hierarchical deep unfolding network for image compressed sensing that enhances expressiveness and adaptability, reduces iterations, and outperforms current methods.
Contribution
It proposes a novel hierarchical unfolding architecture with dynamic hyperparameter generation and acceleration, improving performance and efficiency in image compressed sensing.
Findings
FHDUN saves over 50% of iterative loops compared to recent DUNs.
FHDUN outperforms state-of-the-art CS methods in experiments.
The model adapts hyperparameters dynamically based on input content.
Abstract
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…
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