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 …

Deep learning, in general, focuses on training a neural network from large labeled datasets.
Yet, in many cases, there is value in training a network just from the input at hand. This is …

The deep image prior showed that a randomly initialized network with a suitable architecture
can be trained to solve inverse imaging problems by simply optimizing it's parameters to …

Training deep neural networks has become a common approach for addressing image
restoration problems. An alternative for training a" task-specific" network for each …

Nonblind image deconvolution (NID) is about restoring the latent image with sharp details
from a noisy blurred one using a known blur kernel. This paper presents a dataset-free deep …

Image fusion is utilized in remote sensing (RS) due to the limitation of the imaging sensor
and the high cost of simultaneously acquiring high spatial and spectral resolution images …

Non-blind image deconvolution (NBID) is about restoring a latent sharp image from a
blurred one, given an associated blur kernel. Most existing deep neural networks for NBID …

Physics-inspired deep learning (DL) methods become a research hotspot in solving inverse
scattering problems (ISPs) due to the advantages of imaging quality and speed. However …

In Deep Image Prior (DIP), a Convolutional Neural Network (CNN) is fitted to map a latent
space to a degraded (eg noisy) image but in the process learns to reconstruct the clean …

Neural networks have become a prominent approach to solve inverse problems in recent
years. While a plethora of such methods was developed to solve inverse problems …