The use of deep unfolding networks in compressive sensing (CS) has seen wide
success as they provide both simplicity and interpretability. However, since
most deep unfolding networks are iterative, this incurs significant
redundancies in the network. In this work, we propose a novel recursion-based
framework to enhance the efficiency of deep unfolding models. First, recursions
are used to effectively eliminate the redundancies in deep unfolding networks.
Secondly, we randomize the number of recursions during training to decrease the
overall training time. Finally, to effectively utilize the power of recursions,
we introduce a learnable unit to modulate the features of the model based on
both the total number of iterations and the current iteration index. To
evaluate the proposed framework, we apply it to both ISTA-Net+ and COAST.
Extensive testing shows that our proposed framework allows the network to cut
down as much as 75% of its learnable parameters while mostly maintaining its
performance, and at the same time, it cuts around 21% and 42% from the training
time for ISTA-Net+ and COAST respectively. Moreover, when presented with a
limited training dataset, the recursive models match or even outperform their
respective non-recursive baseline. Codes and pretrained models are available at
this https URL .

本论文提出使用递归来增强深度展开模型的效率，实现消除冗余，减少训练时间和减小可训练参数，并将其应用于 ISTA-Net+ 和 COAST，结果显示递归模型与非递归基线相比，在受限数据集下具有匹配或甚至超过的性能。