Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive. Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM-imaging tasks. Consequently, generalizability becomes a major concern.

The compressive sensing (CS) scheme exploits many fewer measurements than suggested by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has attracted considerable attention in the computational imaging community. While classic image CS schemes employ sparsity using analytical transforms or bases, the learning-based approaches have become increasingly popular in recent years. Such methods can effectively model the structure of image patches by optimizing their sparse representations or learning deep neural networks while preserving the known or modeled sensing process. 

May the year 2023 bring everyone closer to the fulfilment of their dreams. We have left behind a year marked with successes on multiple fronts, including health and technology as well as a year filled with proud people risking their lives for freedom. In particular, two big movements have captured our hearts: the fierce resistance of Ukranian people against the Russian invasion of their country, and the prodemocracy uprising in Iran with women in the lead.

Recent years have witnessed a rapidly growing interest in next-generation imaging systems and their combination with machine learning. While model-based imaging schemes that incorporate physics-based forward models, noise models, and image priors laid the foundation in the emerging field of computational sensing and imaging, recent advances in machine learning, from large-scale optimization to building deep neural networks, are increasingly being applied in modern computational imaging.

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