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 …

Deep convolutional neural networks (CNNs) have attracted considerable interest in low-
level computer vision. Researches are usually devoted to improving the performance via …

Most of the neural networks proposed so far for computational imaging (CI) in optics employ
a supervised training strategy, and thus need a large training set to optimize their weights …

We present a method, Neural Radiance Flow (NeRFlow), to learn a 4D spatial-temporal
representation of a dynamic scene from a set of RGB images. Key to our approach is the use …

In this paper we describe an investigation into the application of deep learning methods for
low-dose and sparse angle computed tomography using small training datasets. To motivate …

Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and
theory that have been accumulated over the years. Recently, this field has been immensely …

Over the last decade, the number of digital images captured per day has increased
exponentially, due to the accessibility of imaging devices. The visual quality of photographs …

We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL)
methodology for continuous representation of measurements. Unlike traditional DL methods …

We propose a novel method for compressed sensing recovery using untrained deep
generative models. Our method is based on the recently proposed Deep Image Prior (DIP) …