# Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer

arXiv: https://arxiv.org/abs/2204.13286 · HTML: https://mlsanity.com/paper/2204.13286/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Sketch-based Manga Retrieval using Manga109 Dataset](https://mlsanity.com/paper/1510.04389/) (2015)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network](https://mlsanity.com/paper/1803.08664/) (2018)
- [Lightweight Image Super-Resolution with Information Multi-distillation Network](https://mlsanity.com/paper/1909.11856/) (2019)
- [Transformer for Single Image Super-Resolution](https://mlsanity.com/paper/2108.11084/) (2021)
- [Fast and Accurate Single Image Super-Resolution via Information Distillation Network](https://mlsanity.com/paper/1803.09454/) (2018)
- [LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond](https://mlsanity.com/paper/2105.10422/) (2021)
- [Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2112.08655/) (2021)
- [A Systematic Survey of Deep Learning-based Single-Image Super-Resolution](https://mlsanity.com/paper/2109.14335/) (2021)
- [Lightweight Image Super-Resolution with Adaptive Weighted Learning Network](https://mlsanity.com/paper/1904.02358/) (2019)

## Built on by (most cited first, 22 of 22)

- [Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2207.02796/) (2022)
- [Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network](https://mlsanity.com/paper/2212.14181/) (2022)
- [Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution](https://mlsanity.com/paper/2308.05022/) (2023)
- [Accelerating Image Super-Resolution Networks with Pixel-Level Classification](https://mlsanity.com/paper/2407.21448/) (2024)
- [Dual-domain Modulation Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2503.10047/) (2025)
- [Fully 1×1 Convolutional Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2307.16140/) (2023)
- [Training-Free Model Ensemble for Single-Image Super-Resolution via Strong-Branch Compensation](https://mlsanity.com/paper/2604.11564/) (2026)
- [Efficient Mixed Transformer for Single Image Super-Resolution](https://mlsanity.com/paper/2305.11403/) (2023)
- [SpatioTemporal Difference Network for Video Depth Super-Resolution](https://mlsanity.com/paper/2508.01259/) (2025)
- [FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution](https://mlsanity.com/paper/2503.10043/) (2025)
- [Efficient Image Super-Resolution via Symmetric Visual Attention Network](https://mlsanity.com/paper/2401.08913/) (2024)
- [Image Super-Resolution using Efficient Striped Window Transformer](https://mlsanity.com/paper/2301.09869/) (2023)
- [A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning](https://mlsanity.com/paper/2302.11184/) (2023)
- [Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution](https://mlsanity.com/paper/2305.17898/) (2023)
- [LATIS: Lambda Abstraction-based Thermal Image Super-resolution](https://mlsanity.com/paper/2311.12046/) (2023)
- [Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2503.16056/) (2025)
- [Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2506.11823/) (2025)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2511.03232/) (2025)
- [Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer](https://mlsanity.com/paper/2312.16455/) (2023)
- [Attention-Guided Multi-scale Interaction Network for Face Super-Resolution](https://mlsanity.com/paper/2409.00591/) (2024)
- [Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mamba](https://mlsanity.com/paper/2510.27296/) (2025)

## Abstract (arXiv)

Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning. However, these methods are difficult to be applied in real-world scenarios since they are inevitably accompanied by the problems of computational and memory costs caused by the complex operations. To solve this issue, we propose a Lightweight Bimodal Network (LBNet) for SISR. Specifically, an effective Symmetric CNN is designed for local feature extraction and coarse image reconstruction. Meanwhile, we propose a Recursive Transformer to fully learn the long-term dependence of images thus the global information can be fully used to further refine texture details. Studies show that the hybrid of CNN and Transformer can build a more efficient model. Extensive experiments have proved that our LBNet achieves more prominent performance than other state-of-the-art methods with a relatively low computational cost and memory consumption. The code is available at https://github.com/IVIPLab/LBNet.