Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from
the observed LR images. As SR has been developed for more than three decades, both …

Super-resolution, the process of obtaining one or more high-resolution images from one or
more low-resolution observations, has been a very attractive research topic over the last two …

As a new earth observation tool, satellite video has been widely used in remote-sensing
field for dynamic analysis. Video super-resolution (VSR) technique has thus attracted …

Convolutional neural networks (CNN) are a special type of deep neural networks (DNN).
They have so far been successfully applied to image super-resolution (SR) as well as other …

Video super-resolution (VSR) has become one of the most critical problems in video
processing. In the deep learning literature, recent works have shown the benefits of using …

Super-resolution reconstruction technology is a crucial approach to enhance the quality of
remote sensing optical images. Currently, the mainstream reconstruction methods leverage …

We present a general method for blind image deconvolution using Bayesian inference with
super-Gaussian sparse image priors. We consider a large family of priors suitable for …

Real-world single image denoising is crucial and practical in computer vision. Bayesian
inversions combined with score priors now have proven effective for single image denoising …

Capturing ground truth data to benchmark super-resolution (SR) is challenging. Therefore,
current quantitative studies are mainly evaluated on simulated data artificially sampled from …

In this paper, we propose a variational Bayesian method for Retinex to simulate and
interpret how the human visual system perceives color. To construct a hierarchical Bayesian …