A Two-Stage Attentive Network for Single Image Super-Resolution

04/21/2021
by   Jiqing Zhang, et al.
0

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress. However, most of the existing CNNs-based SISR methods do not adequately explore contextual information in the feature extraction stage and pay little attention to the final high-resolution (HR) image reconstruction step, hence hindering the desired SR performance. To address the above two issues, in this paper, we propose a two-stage attentive network (TSAN) for accurate SISR in a coarse-to-fine manner. Specifically, we design a novel multi-context attentive block (MCAB) to make the network focus on more informative contextual features. Moreover, we present an essential refined attention block (RAB) which could explore useful cues in HR space for reconstructing fine-detailed HR image. Extensive evaluations on four benchmark datasets demonstrate the efficacy of our proposed TSAN in terms of quantitative metrics and visual effects. Code is available at https://github.com/Jee-King/TSAN.

READ FULL TEXT

page 1

page 3

page 6

page 7

page 10

page 11

page 12

page 14

05/29/2022

Image Super-resolution with An Enhanced Group Convolutional Neural Network

CNNs with strong learning abilities are widely chosen to resolve super-r...
05/21/2020

Single Image Super-Resolution via Residual Neuron Attention Networks

Deep Convolutional Neural Networks (DCNNs) have achieved impressive perf...
11/13/2020

Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning

Despite convolutional network-based methods have boosted the performance...
07/09/2019

Gated Multiple Feedback Network for Image Super-Resolution

The rapid development of deep learning (DL) has driven single image supe...
02/24/2018

Single Image Super-Resolution via Cascaded Multi-Scale Cross Network

The deep convolutional neural networks have achieved significant improve...
03/25/2021

Asymmetric CNN for image super-resolution

Deep convolutional neural networks (CNNs) have been widely applied for l...
07/26/2016

End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks

One impressive advantage of convolutional neural networks (CNNs) is thei...