Phase recovery (PR) refers to calculating the phase of the light field from its intensity
measurements. As exemplified from quantitative phase imaging and coherent diffraction …

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 …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Convolutional Neural Networks (CNNs) are highly effective for image reconstruction
problems. Typically, CNNs are trained on large amounts of training images. Recently …

This paper proposes a deep learning method for low-light image enhancement, which
exploits the generation capability of Neural Networks (NNs) while requiring no training …

In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …

The linear inverse problem is fundamental to the development of various scientific areas.
Innumerable attempts have been carried out to solve different variants of the linear inverse …

Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

Un-trained convolutional neural networks have emerged as highly successful tools for
image recovery and restoration. They are capable of solving standard inverse problems …

Convolutional Neural Networks (CNNs) have emerged as highly successful tools for image
generation, recovery, and restoration. A major contributing factor to this success is that …