Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …

Despite convolutional network-based methods have boosted the performance of single
image super-resolution (SISR), the huge computation costs restrict their practical …

The super-resolution (SR) reconstruction of remote sensing images is a low-cost and
efficient method to improve their resolution, and it is often used for further image analysis. To …

Light field imaging has emerged as a very promising technology in the field of computational
photography. Cameras are becoming commercially available for capturing real-world light …

In this paper, we propose a spatial super-resolution method for light fields, which combines
the SR-BM3D single image super-resolution filter and the recently introduced LFBM5D light …

This paper presents an end-to-end multi-scale deep encoder (convolution) and decoder
(deconvolution) network for single image super-resolution (SISR) guided by phase …

Single image super-resolution (SISR) is an ill-posed problem aiming at estimating a
plausible high-resolution (HR) image from a single low-resolution image. Current state-of …

Abstract We propose the Anchored Regression Network (ARN), a nonlinear regression
network which can be seamlessly integrated into various networks or can be used stand …

Modern deep learning super-resolution approaches have achieved remarkable
performance where the low-resolution (LR) input is a degraded high-resolution (HR) image …

Single image super-resolution (SISR) is a challenging work, which aims to recover the
missing information in an observed low-resolution (LR) image and generate the …