Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …

As an emerging paradigm for signal acquisition and reconstruction, compressive sensing
(CS) achieves high-speed sampling and compression jointly and has found its way into …

Standard diffusion models involve an image transform--adding Gaussian noise--and an
image restoration operator that inverts this degradation. We observe that the generative …

Many interesting tasks in image restoration can be cast as linear inverse problems. A recent
family of approaches for solving these problems uses stochastic algorithms that sample from …

Deep neural networks provide unprecedented performance gains in many real-world
problems in signal and image processing. Despite these gains, the future development and …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods utilize mathematical …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

Compressive sensing (CS) is an effective technique for reconstructing image from a small
amount of sampled data. It has been widely applied in medical imaging, remote sensing …

DL has shown great potential to revolutionize communication systems. This article provides
an overview of the recent advancements in DL-based physical layer communications. DL …