Single image super-resolution based on trainable feature matching attention network-Reference-Cited by-同舟云学术

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Single image super-resolution based on trainable feature matching attention network

Published:2024-05 Issue: Volume:149 Page:110289
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Chen Qizhou,Shao Qing^ORCID

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

Publisher

Elsevier BV

Reference43 articles.

1. Image super-resolution using deep convolutional networks;Dong;IEEE Trans. Pattern Anal. Mach. Intell.,2016

2. Accurate image super-resolution using very deep convolutional networks;Kim,2016

3. Image super-resolution using very deep residual channel attention networks;Zhang,2018

4. Srfeat: Single image super-resolution with feature discrimination;Park,2018

5. Patch loss: A generic multi-scale perceptual loss for single image super-resolution;An;Pattern Recognit.,2023

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1. SANet: Face super-resolution based on self-similarity prior and attention integration;Pattern Recognition;2025-01

2. DIVA: Deep unfolded network from quantum interactive patches for image restoration;Pattern Recognition;2024-11

3. Batch-transformer for scene text image super-resolution;The Visual Computer;2024-08-29

4. AM-ESRGAN: Super-Resolution Reconstruction of Ancient Murals Based on Attention Mechanism and Multi-Level Residual Network;Electronics;2024-08-08

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