Low-light photography presents significant challenges. Multi-image processing methods
have made numerous attempts to obtain high-quality photos yet remain unsatisfactory …

The first mobile camera phone was sold only 20 years ago, when taking pictures with one's
phone was an oddity, and sharing pictures online was unheard of. Today, the smartphone is …

Recently, deep convolutional neural networks (CNNs) have been successfully applied to the
single-image super-resolution (SISR) task with great improvement in terms of both peak …

Modern handheld devices can acquire burst image sequence in a quick succession.
However, the individual acquired frames suffer from multiple degradations and are …

While single-image super-resolution (SISR) has attracted substantial interest in recent years,
the proposed approaches are limited to learning image priors in order to add high frequency …

Establishing dense correspondences between a pair of images is an important and general
problem. However, dense flow estimation is often inaccurate in the case of large …

Our work explores temporal self-supervision for GAN-based video generation tasks. While
adversarial training successfully yields generative models for a variety of areas, temporal …

Homography estimation is a basic image alignment method in many applications. It is
usually conducted by extracting and matching sparse feature points, which are error-prone …

Point-scanning imaging systems are among the most widely used tools for high-resolution
cellular and tissue imaging, benefiting from arbitrarily defined pixel sizes. The resolution …

This work addresses the Burst Super-Resolution (BurstSR) task using a new architecture,
which requires restoring a high-quality image from a sequence of noisy, misaligned, and low …