Abstract Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and
interpretable method for solving various inverse problems by utilizing any off-the-shelf …

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly
serve as the image prior for model-based methods to solve many inverse problems. Such a …

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks
toward solving image denoising, existing methods mostly rely on simple noise assumptions …

Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving
computational imaging problems through the integration of physical models and learned …

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

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

In this paper, we employ the emerging paradigm of physics-informed neural networks
(PINNs) for the solution of representative inverse scattering problems in photonic …

Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …

Deep neural networks have provided state-of-the-art solutions for problems such as image
denoising, which implicitly rely on a prior probability model of natural images. Two recent …

Inverse problems are essential to imaging applications. In this letter, we propose a model-
based deep learning network, named FISTA-Net, by combining the merits of interpretability …