A deep convolutional neural network with selection units for super-resolution
Rectified linear units (ReLU) are known to be effective in many deep learning methods.
Inspired by linear-mapping technique used in other super-resolution (SR) methods, we …
Inspired by linear-mapping technique used in other super-resolution (SR) methods, we …
Mulut: Cooperating multiple look-up tables for efficient image super-resolution
The high-resolution screen of edge devices stimulates a strong demand for efficient image
super-resolution (SR). An emerging research, SR-LUT, responds to this demand by …
super-resolution (SR). An emerging research, SR-LUT, responds to this demand by …
Image super-resolution using a dilated convolutional neural network
G Lin, Q Wu, L Qiu, X Huang - Neurocomputing, 2018 - Elsevier
Image super-resolution (SR) has attracted great attention due to its wide practical
applications. The objective of SR is to reconstruct high-resolution images from low …
applications. The objective of SR is to reconstruct high-resolution images from low …
GUN: Gradual upsampling network for single image super-resolution
In this paper, an efficient super-resolution (SR) method based on deep convolutional neural
network (CNN) is proposed, namely gradual upsampling network (GUN). Recent CNN …
network (CNN) is proposed, namely gradual upsampling network (GUN). Recent CNN …
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
In recent years, many studies have been conducted on image super-resolution (SR).
However, to the best of our knowledge, few SR methods are concerned with compressed …
However, to the best of our knowledge, few SR methods are concerned with compressed …
Single image super-resolution via adaptive transform-based nonlocal self-similarity modeling and learning-based gradient regularization
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 …
missing information in an observed low-resolution (LR) image and generate the …
Single-image super-resolution via an iterative reproducing kernel Hilbert space method
Image super-resolution (SR), a process to enhance image resolution, has important
applications in satellite imaging, high-definition television, medical imaging, and so on …
applications in satellite imaging, high-definition television, medical imaging, and so on …
Single image super-resolution using global regression based on multiple local linear mappings
JS Choi, M Kim - IEEE transactions on image processing, 2017 - ieeexplore.ieee.org
Super-resolution (SR) has become more vital, because of its capability to generate high-
quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) …
quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) …
Fast image super-resolution via local adaptive gradient field sharpening transform
This paper proposes a single-image super-resolution scheme by introducing a gradient field
sharpening transform that converts the blurry gradient field of upsampled low-resolution (LR) …
sharpening transform that converts the blurry gradient field of upsampled low-resolution (LR) …
Super-Interpolation With Edge-Orientation-Based Mapping Kernels for Low Complex Upscaling
With the advent of ultrahigh-definition (UHD) video services, super-resolution (SR)
techniques are often required to generate high-resolution (HR) images from low-resolution …
techniques are often required to generate high-resolution (HR) images from low-resolution …