Image super-resolution (SR) is an interesting topic in computer vision. However, it remains
challenging to achieve high-resolution image from the corresponding low-resolution version …

This paper proposes a new model for single image super-resolution (SR) task by utilizing
the design of densely connected convolutional networks (DenseNet). The proposed method …

Recently, most deep convolutional neural networks used for image super‐resolution have
achieved impressive performance on ideal datasets. However, these methods always fail in …

Recently, single image super-resolution (SISR), aiming to preserve the lost structural and
textural information from the input low resolution image, has witnessed huge demand from …

Althoughtheaccuracyofsuper-resolution (SR) methodsbased on convolutional neural
networks (CNN) soars high, the complexity and computation also explode with the increased …

In this paper, we propose a new image super-resolution (SR) approach based on a
convolutional neural network (CNN), which jointly learns the feature extraction, upsampling …

Image super-resolution (SR) is a fundamental technique in the field of image processing and
computer vision. Recently, deep learning has witnessed remarkable progress in many super …

Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve
the resolution of images or videos in computer vision. In recent years, significant progress …

Single image super-resolution (SR) have recently shown great performance thanks to the
advances in deep learning. In the middle of the deep networks for SR, a part that increases …

Super-resolution (SR) is a long-standing problem in image processing and computer vision
and has attracted great attention from researchers over the decades. The main concept of …