# Swift Parameter-free Attention Network for Efficient Super-Resolution

arXiv: https://arxiv.org/abs/2311.12770 · HTML: https://mlsanity.com/paper/2311.12770/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 58 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 30 of 30)

- [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)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [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)
- [AiATrack: Attention in Attention for Transformer Visual Tracking](https://mlsanity.com/paper/2207.09603/) (2022)
- [Attention-based Dropout Layer for Weakly Supervised Object Localization](https://mlsanity.com/paper/1908.10028/) (2019)
- [Residual Feature Distillation Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2009.11551/) (2020)
- [Structure-Preserving Super Resolution with Gradient Guidance](https://mlsanity.com/paper/2003.13081/) (2020)
- [LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond](https://mlsanity.com/paper/2105.10422/) (2021)
- [CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution](https://mlsanity.com/paper/2212.04362/) (2022)
- [Omni Aggregation Networks for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2304.10244/) (2023)
- [Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution](https://mlsanity.com/paper/2302.13800/) (2023)
- [Residual Local Feature Network for Efficient Super-Resolution](https://mlsanity.com/paper/2205.07514/) (2022)
- [N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution](https://mlsanity.com/paper/2211.11436/) (2022)
- [ShuffleMixer: An Efficient ConvNet for Image Super-Resolution](https://mlsanity.com/paper/2205.15175/) (2022)
- [DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition](https://mlsanity.com/paper/2203.10233/) (2022)
- [Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2112.08655/) (2021)
- [Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets](https://mlsanity.com/paper/2003.13549/) (2020)
- [Multi-Attention Based Ultra Lightweight Image Super-Resolution](https://mlsanity.com/paper/2008.12912/) (2020)
- [RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization](https://mlsanity.com/paper/2205.05671/) (2022)
- [Memory-Augmented Non-Local Attention for Video Super-Resolution](https://mlsanity.com/paper/2108.11048/) (2021)
- [Learning Prior Feature and Attention Enhanced Image Inpainting](https://mlsanity.com/paper/2208.01837/) (2022)
- [Parameter-Free Channel Attention for Image Classification and Super-Resolution](https://mlsanity.com/paper/2303.11055/) (2023)

## Built on by (most cited first, 24 of 24)

- [The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2404.10343/) (2024)
- [NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results](https://mlsanity.com/paper/2404.09790/) (2024)
- [The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2504.10686/) (2025)
- [The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2604.03198/) (2026)
- [Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey](https://mlsanity.com/paper/2404.16484/) (2024)
- [Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach](https://mlsanity.com/paper/2401.05633/) (2024)
- [NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Results](https://mlsanity.com/paper/2504.14582/) (2025)
- [MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices](https://mlsanity.com/paper/2507.01838/) (2025)
- [MIPI 2024 Challenge on Demosaic for HybridEVS Camera: Methods and Results](https://mlsanity.com/paper/2405.04867/) (2024)
- [NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge](https://mlsanity.com/paper/2506.01394/) (2025)
- [EcoWeedNet: A Lightweight and Automated Weed Detection Method for Sustainable Next-Generation Agricultural Consumer Electronics](https://mlsanity.com/paper/2502.00205/) (2025)
- [SR-Prominence: A Crowdsourced Protocol and Dataset Suite for Perceptually-Weighted Super-Resolution Artifact Evaluation](https://mlsanity.com/paper/2605.14847/) (2026)
- [Breaking the Attention Bottleneck](https://mlsanity.com/paper/2406.10906/) (2024)
- [HASN: Hybrid Attention Separable Network for Efficient Image Super-resolution](https://mlsanity.com/paper/2410.09844/) (2024)
- [Prominence-Aware Artifact Detection and Dataset for Image Super-Resolution](https://mlsanity.com/paper/2510.16752/) (2025)
- [SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation](https://mlsanity.com/paper/2605.01931/) (2026)
- [A Multi-scenario Attention-based Generative Model for Personalized Blood Pressure Time Series Forecasting](https://mlsanity.com/paper/2409.04704/) (2024)
- [Bit-depth color recovery via off-the-shelf super-resolution models](https://mlsanity.com/paper/2501.05611/) (2025)
- [DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution](https://mlsanity.com/paper/2505.21262/) (2025)
- [Task-driven real-world super-resolution of document scans](https://mlsanity.com/paper/2506.06953/) (2025)
- [Efficient Perceptual Image Super Resolution: AIM 2025 Study and Benchmark](https://mlsanity.com/paper/2510.12765/) (2025)
- [A Cosine Network for Image Super-Resolution](https://mlsanity.com/paper/2601.16413/) (2026)
- [Exploring Real-Time Super-Resolution: Benchmarking and Fine-Tuning for Streaming Content](https://mlsanity.com/paper/2602.11339/) (2026)
- [KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry](https://mlsanity.com/paper/2603.04755/) (2026)

## Abstract (arXiv)

Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision, aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional attention mechanisms have significantly improved SISR performance but often result in complex network structures and large number of parameters, leading to slow inference speed and large model size. To address this issue, we propose the Swift Parameter-free Attention Network (SPAN), a highly efficient SISR model that balances parameter count, inference speed, and image quality. SPAN employs a novel parameter-free attention mechanism, which leverages symmetric activation functions and residual connections to enhance high-contribution information and suppress redundant information. Our theoretical analysis demonstrates the effectiveness of this design in achieving the attention mechanism's purpose. We evaluate SPAN on multiple benchmarks, showing that it outperforms existing efficient super-resolution models in terms of both image quality and inference speed, achieving a significant quality-speed trade-off. This makes SPAN highly suitable for real-world applications, particularly in resource-constrained scenarios. Notably, we won the first place both in the overall performance track and runtime track of the NTIRE 2024 efficient super-resolution challenge. Our code and models are made publicly available at https://github.com/hongyuanyu/SPAN.