In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm
for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as …

In this article, we review recent uses of convolutional neural networks (CNNs) to solve
inverse problems in imaging. It has recently become feasible to train deep CNNs on large …

This paper presents an extension of the family of orthogonal Battle/Lemarié spline wavelet
transforms with emphasis on filter bank implementation. Spline wavelets that are not …

This paper presents an account of the current state of sampling, 50 years after Shannon's
formulation of the sampling theorem. The emphasis is on regular sampling, where the grid is …

This paper describes a new approach to the characterization of texture properties at
multiple scales using the wavelet transform. The analysis uses an overcomplete wavelet …

We propose a general methodology (PURE-LET) to design and optimize a wide class of
transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-…

… much indebted to Annette Unser for her artistic interpretation of the fractional B-splines (cover)
and her colorful illustration of the two-scale relation (Pascal’s triangle). Michael Unser is a …

We present an automatic subpixel registration algorithm that minimizes the mean square
intensity difference between a reference and a test data set, which can be either images (two-…

A new method based on the Young–Laplace equation for measuring contact angles and
surface tensions is presented. In this approach, a first-order perturbation technique helps to …

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 as …