Image super-resolution: The techniques, applications, and future
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 …
the observed LR images. As SR has been developed for more than three decades, both …
Super-resolution: a comprehensive survey
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 …
more low-resolution observations, has been a very attractive research topic over the last two …
Enhanced deep residual networks for single image super-resolution
Recent research on super-resolution has progressed with the development of deep
convolutional neural networks (DCNN). In particular, residual learning techniques exhibit …
convolutional neural networks (DCNN). In particular, residual learning techniques exhibit …
Photo-realistic single image super-resolution using a generative adversarial network
Despite the breakthroughs in accuracy and speed of single image super-resolution using
faster and deeper convolutional neural networks, one central problem remains largely …
faster and deeper convolutional neural networks, one central problem remains largely …
Enhancenet: Single image super-resolution through automated texture synthesis
Single image super-resolution is the task of inferring a high-resolution image from a single
low-resolution input. Traditionally, the performance of algorithms for this task is measured …
low-resolution input. Traditionally, the performance of algorithms for this task is measured …
Single-image super-resolution: A benchmark
Single-image super-resolution is of great importance for vision applications, and numerous
algorithms have been proposed in recent years. Despite the demonstrated success, these …
algorithms have been proposed in recent years. Despite the demonstrated success, these …
Convolutional sparse coding for image super-resolution
Remote sensing image super-resolution via mixed high-order attention network
Recently, remote sensing images have become increasingly popular in a number of tasks,
such as environmental monitoring. However, the observed images from satellite sensors …
such as environmental monitoring. However, the observed images from satellite sensors …
Single image super-resolution with non-local means and steering kernel regression
Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is
important to design an effective prior. For this purpose, we propose a novel image SR …
important to design an effective prior. For this purpose, we propose a novel image SR …