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

Blind image super-resolution (SR), aiming to super-resolve low-resolution images with
unknown degradation, has attracted increasing attention due to its significance in promoting …

Abstract Model-based optimization methods and discriminative learning methods have been
the two dominant strategies for solving various inverse problems in low-level vision …

While deep neural networks (DNN) based single image super-resolution (SISR) methods
are rapidly gaining popularity, they are mainly designed for the widely-used bicubic …

Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …

Deep neural networks (DNNs) have shown very promising results for various image
restoration (IR) tasks. However, the design of network architectures remains a major …

Alternating direction method of multiplier (ADMM) is a widely used algorithm for solving
constrained optimization problems in image restoration. Among many useful features, one …

While variational methods have been among the most powerful tools for solving linear
inverse problems in imaging, deep (convolutional) neural networks have recently taken the …

Abstract Plug-and-play (PnP) is a non-convex framework that integrates modern denoising
priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal …

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 …