Self-Supervised Scalable Deep Compressed Sensing
Bin Chen Xuanyu Zhang Shuai Liu Yongbing Zhang Jian Zhang
Compressed sensing (CS) is a promising tool for reducing sampling costs.
Current deep neural network (NN)-based CS methods face challenges in collecting
labeled measurement-ground truth (GT) data and generalizing to real
applications. This paper proposes a novel $\mathbf{S}$elf-supervised
s$\mathbf{C}$alable deep CS method, comprising a $\mathbf{L}$earning scheme
called $\mathbf{SCL}$ and a family of $\mathbf{Net}$works named
$\mathbf{SCNet}$, which does not require GT and can handle arbitrary sampling
ratios and matrices once trained on a partial measurement set. Our SCL contains
a dual-domain loss and a four-stage recovery strategy. The former encourages a
cross-consistency on two measurement parts and a sampling-reconstruction
cycle-consistency regarding arbitrary ratios and matrices to maximize
data/information utilization. The latter can progressively leverage common
signal prior in external measurements and internal characteristics of test
samples and learned NNs to improve accuracy. SCNet combines the explicit
guidance from optimization algorithms with implicit regularization from
advanced NN blocks to learn a collaborative signal representation. Our
theoretical analyses and experiments on simulated and real captured data,
covering 1-/2-/3-D natural and scientific signals, demonstrate the
effectiveness, superior performance, flexibility, and generalization ability of
our method over existing self-supervised methods and its significant potential
in competing against state-of-the-art supervised methods.