In surveillance, monitoring and tactical reconnaissance, gathering visual information from a
dynamic environment and accurately processing such data are essential to making informed …

Images captured in low-light conditions usually suffer from very low contrast, which
increases the difficulty of subsequent computer vision tasks in a great extent. In this paper, a …

Many fundamental image-related problems involve deconvolution operators. Real blur
degradation seldom complies with an deal linear convolution model due to camera noise …

PET imaging involves radiotracer injections, raising concerns about the risk of radiation
exposure. To minimize the potential risk, one way is to reduce the injected tracer. However …

While variational methods have been among the most powerful tools for solving linear
inverse problems in imaging, deep (convolutional) neural networks have recently taken the …

Collaborative filters perform denoising through transform-domain shrinkage of a group of
similar patches extracted from an image. Existing collaborative filters of stationary correlated …

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 …

Traditional patch-based sparse representation modeling of natural images usually suffer
from two problems. First, it has to solve a large-scale optimization problem with high …

Sparse representation models code an image patch as a linear combination of a few atoms
chosen out from an over-complete dictionary, and they have shown promising results in …

Learning good image priors is of utmost importance for the study of vision, computer vision
and image processing applications. Learning priors and optimizing over whole images can …