The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report

Gradient-based learning applied to document recognition

Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998

Earlier work this paper cites.

Pattern recognition and machine learning

Christopher M Bishop and Nasser M Nasrabadi · 2006

Earlier work this paper cites.

Space-time super-resolution using graph-cut optimization

Uma Mudenagudi, Subhashis Banerjee, and Prem Kumar Kalra · 2011

Earlier work this paper cites.

Empirical evaluation of gated recurrent neural networks on sequence modeling

Original

Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio · 2014

Earlier work this paper cites.

Adam: A method for stochastic optimization

Original

Diederik P Kingma and Jimmy Ba · 2014

Earlier work this paper cites.

Gaussian error linear units (gelus)

Original

Dan Hendrycks and Kevin Gimpel · 2016

Earlier work this paper cites.

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016

Earlier work this paper cites.

NTIRE 2017 challenge on single image super-resolution: Dataset and study

Eirikur Agustsson and Radu Timofte · 2017

Earlier work this paper cites.

MobileNets: Efficient convolutional neural networks for mobile vision applications

Original

Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017

Earlier work this paper cites.

Photo-realistic single image super-resolution using a generative adversarial network

Christian Ledig, Lucas Theis, Ferenc Husz’ar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017

Earlier work this paper cites.

Enhanced deep residual networks for single image super-resolution

Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017

Earlier work this paper cites.

Sgdr: Stochastic gradient descent with warm restarts

Ilya Loshchilov and Frank Hutter · 2017

Earlier work this paper cites.

NTIRE 2017 challenge on single image super-resolution: Methods and results

Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, Lei Zhang, et al · 2017

Earlier work this paper cites.

Attention is all you need

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017

Earlier work this paper cites.

Neural architecture search with reinforcement learning

Barret Zoph and Quoc V Le · 2017

Earlier work this paper cites.

Evidence-based framework for multi-image super-resolution

Ujwala Patil, Ramesh Ashok Tabib, Channabasappa M. Konin, and Uma Mudenagudi · 2018

Earlier work this paper cites.

Learning transferable architectures for scalable image recognition

Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018

Earlier work this paper cites.

Lightweight image super-resolution with information multi-distillation network

Zheng Hui, Xinbo Gao, Yunchu Yang, and Xiumei Wang · 2019

Earlier work this paper cites.

MetaPruning: Meta learning for automatic neural network channel pruning

Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Tim Kwang-Ting Cheng, and Jian Sun · 2019

Earlier work this paper cites.

Pytorch: An imperative style, high-performance deep learning library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019

Earlier work this paper cites.

DHP: Differentiable meta pruning via hypernetworks

Yawei Li, Shuhang Gu, Kai Zhang, Luc Van Gool, and Radu Timofte · 2020

Earlier work this paper cites.

Residual feature distillation network for lightweight image super-resolution

Jie Liu, Jie Tang, and Gangshan Wu · 2020

Earlier work this paper cites.

Rethinking coarse-to-fine approach in single image deblurring

Sung-Jin Cho, Seo-Won Ji, Jun-Pyo Hong, Seung-Won Jung, and Sung-Jea Ko · 2021

Earlier work this paper cites.

Anchor-based plain net for mobile image super-resolution

Zongcai Du, Jie Liu, Jie Tang, and Gangshan Wu · 2021

Earlier work this paper cites.

Swinir: Image restoration using swin transformer

Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021

Cited alongside, same era.

Adadm: Enabling normalization for image super-resolution

Original

Jie Liu, Jie Tang, and Gangshan Wu · 2021

Cited alongside, same era.

Real-esrgan: Training real-world blind super-resolution with pure synthetic data

Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan · 2021

Cited alongside, same era.

Simam: A simple, parameter-free attention module for convolutional neural networks

Lingxiao Yang, Ru-Yuan Zhang, Lida Li, and Xiaohua Xie · 2021

Cited alongside, same era.

Multi-stage progressive image restoration

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao · 2021

Cited alongside, same era.

Spatially-adaptive feature modulation for efficient image super-resolution

Long Sun, Jiangxin Dong, Jinhui Tang, and Jinshan Pan · 2023

Later among the works it cites.

Swift parameter-free attention network for efficient super-resolution

Original

Cheng Wan, Hongyuan Yu, Zhiqi Li, Yihang Chen, Yajun Zou, Yuqing Liu, Xuanwu Yin, and Kunlong Zuo · 2023

Later among the works it cites.

A single residual network with esa modules and distillation

Yucong Wang and Minjie Cai · 2023

Later among the works it cites.

Dipnet: Efficiency distillation and iterative pruning for image super-resolution

Lei Yu, Xinpeng Li, Youwei Li, Ting Jiang, Qi Wu, Haoqiang Fan, and Shuaicheng Liu · 2023

Later among the works it cites.

Single image super-resolution: a comprehensive review and recent insight

Hanadi Al-Mekhlafi and Shiguang Liu · 2024

Closest in time.