Image super-resolution (SR) is one of the vital image processing methods that improve the
resolution of an image in the field of computer vision. In the last two decades, significant …

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 …

Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …

With the goal of recovering high-quality image content from its degraded version, image
restoration enjoys numerous applications, such as in surveillance, computational …

Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …

Structures matter in single image super resolution (SISR). Recent studies benefiting from
generative adversarial network (GAN) have promoted the development of SISR by …

We consider image transformation problems, where an input image is transformed into an
output image. Recent methods for such problems typically train feed-forward convolutional …

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 …

Deep convolutional neural networks (CNNs) have been widely applied for low-level vision
over the past five years. According to the nature of different applications, designing …

Existing methods for single image super-resolution (SR) are typically evaluated with
synthetic degradation models such as bicubic or Gaussian downsampling. In this paper, we …