In recent years, deep learning has achieved remarkable empirical success for image
reconstruction. This has catalyzed an ongoing quest for the precise characterization of the …

A new type of spectrometer that heavily relies on computational technique to recover
spectral information is introduced. They are different from conventional optical spectrometers …

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 …

Recent efforts on solving inverse problems in imaging via deep neural networks use
architectures inspired by a fixed number of iterations of an optimization method. The number …

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

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 …

We consider using untrained neural networks to solve the reconstruction problem of
snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to …

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

We present a new image reconstruction method that replaces the projector in a projected
gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained …