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 (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 …

Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …

Learning-based single image super-resolution (SISR) methods are continuously showing
superior effectiveness and efficiency over traditional model-based methods, largely due to …

Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially
invariant across the whole image. However, such an assumption is rarely applicable for real …

Medical imaging plays a critical role in various clinical applications. However, due to
multiple considerations such as cost and radiation dose, the acquisition of certain image …

Kernel estimation is generally one of the key problems for blind image super-resolution
(SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior …

Numerous single-image super-resolution algorithms have been proposed in the literature,
but few studies address the problem of performance evaluation based on visual perception …

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 …

This paper deals with the single image scale-up problem using sparse-representation
modeling. The goal is to recover an original image from its blurred and down-scaled noisy …