To more efficiently address image compressed sensing (CS) problems, we present a novel
content-aware scalable network dubbed CASNet which collectively achieves adaptive …

Terahertz (THz) waves show great potential in nondestructive testing, biodetection and
cancer imaging. Despite recent progress in THz wave near-field probes/apertures enabling …

Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in
imaging, image processing, and computer vision. INNs combine regression NNs and an …

We introduce a general framework for active learning in regression problems. Our
framework extends the standard setup by allowing for general types of data, rather than …

In “extreme” computational imaging that collects extremely undersampled or noisy
measurements, obtaining an accurate image within a reasonable computing time is …

Convolutional operator learning is gaining attention in many signal processing and
computer vision applications. Learning kernels has mostly relied on so-called patch-domain …

In recent years, the use of sparse recovery techniques in the approximation of high-
dimensional functions has garnered increasing interest. In this work we present a survey of …

Image reconstruction based on indirect, noisy, or incomplete data remains an important yet
challenging task. While methods such as compressive sensing have demonstrated high …

We introduce and analyse a framework for function interpolation using compressed sensing.
This framework—which is based on weighted ℓ^ 1 ℓ 1 minimization—does not require a …

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it
a pivotal technology in the context of sensor data acquisition. With the proliferation of image …