Conference Paper2024Cited by 1

Lightweight Recurrent Neural Network for Image Super-resolution

Mir Sazzat Hossain, AKM Mahbubur Rahman, Md. Ashraful Amin, Amin Ahsan Ali

IEEE Int'l Conf on Image Processing

IEEE, pp. 1567-1573

DOI: 10.1109/icip51287.2024.10647844URL

Abstract

In recent years, significant progress has been made in image super-resolution through the use of large-scale models. However, the efficacy of these models comes at the cost of their substantial size, posing challenges and limitations when deploying them on resource-constrained devices. Despite their remarkable performance, the feasibility of employing such models on low-end devices has remained a contentious topic. In light of this, our research introduces a lightweight approach to image super-resolution, leveraging a simple recurrent neural network architecture consisting of a recurrent convolution block. Our proposed model uses less than 75k parameters, which is 10 times fewer than the state-of-the-art transformer-based super-resolution model. Despite its small size, the proposed model performs well in image super-resolution tasks both visually and quantitatively. Our work presents a promising direction for addressing the difficulty of deploying efficient super-resolution models on resource-limited devices.

Keywords

  • image super-resolution
  • recurrent neural networks
  • lightweight neural networks
  • resource-constrained devices
  • model efficiency
  • parameter reduction

CCDS Authors

Mir Sazzat Hossain
Mir Sazzat Hossain

Mir Sazzat Hossain

  • Research Assistant, CCDS

Bachelors

Independent University, Bangladesh

Published at

WACV, IJCNN, ICIP, Smart Agricultural Technology

Research Interests

Machine Learning, Computer Vision

AKM Mahbubur Rahman, PhD
AKM Mahbubur Rhman, PhD

AKM Mahbubur Rahman, PhD

  • Director, DS & IPT Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, ECCV, WACV, PAKDD, LREC, IJCNN, ICPR, ICIP, HCII, BEA, Scientific Reports, Information Sciences

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

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Cited By

  1. 1.Sunwoo Cho, Yejin Jung, Nam Ik Cho, Jae Woong Soh. (2025). Dataset Distillation for Super-Resolution Without Class Labels and Pre-Trained Models. IEEE Signal Processing Letters, 32, 3700–3704[10.1109/lsp.2025.3611694]
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