# ShuffleMixer: An Efficient ConvNet for Image Super-Resolution

arXiv: https://arxiv.org/abs/2205.15175 · HTML: https://mlsanity.com/paper/2205.15175/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 53 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 30 of 38)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [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)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [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)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [Sketch-based Manga Retrieval using Manga109 Dataset](https://mlsanity.com/paper/1510.04389/) (2015)
- [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021)
- [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)

## Built on by (most cited first, 30 of 45)

- [VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks](https://mlsanity.com/paper/2504.05118/) (2025)
- [Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution](https://mlsanity.com/paper/2302.13800/) (2023)
- [Multi-scale Attention Network for Single Image Super-Resolution](https://mlsanity.com/paper/2209.14145/) (2022)
- [The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2504.10686/) (2025)
- [Swift Parameter-free Attention Network for Efficient Super-Resolution](https://mlsanity.com/paper/2311.12770/) (2023)
- [See More Details: Efficient Image Super-Resolution by Experts Mining](https://mlsanity.com/paper/2402.03412/) (2024)
- [Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach](https://mlsanity.com/paper/2401.05633/) (2024)
- [IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model](https://mlsanity.com/paper/2405.09873/) (2024)
- [Infrared Image Super-Resolution: Systematic Review, and Future Trends](https://mlsanity.com/paper/2212.12322/) (2022)
- [Learning from Multi-Perception Features for Real-Word Image Super-resolution](https://mlsanity.com/paper/2305.18547/) (2023)
- [Dual-domain Modulation Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2503.10047/) (2025)
- [Partial Large Kernel CNNs for Efficient Super-Resolution](https://mlsanity.com/paper/2404.11848/) (2024)
- [Fully 1×1 Convolutional Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2307.16140/) (2023)
- [Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey](https://mlsanity.com/paper/2404.16223/) (2024)
- [Emulating Self-attention with Convolution for Efficient Image Super-Resolution](https://mlsanity.com/paper/2503.06671/) (2025)
- [Multi-Depth Branch Network for Efficient Image Super-Resolution](https://mlsanity.com/paper/2309.17334/) (2023)
- [PromptSR: Cascade Prompting for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2507.04118/) (2025)
- [WaveDH: Wavelet Sub-bands Guided ConvNet for Efficient Image Dehazing](https://mlsanity.com/paper/2404.01604/) (2024)
- [Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem](https://mlsanity.com/paper/2409.03179/) (2024)
- [FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution](https://mlsanity.com/paper/2503.10043/) (2025)
- [Bridging Fidelity-Reality with Controllable One-Step Diffusion for Image Super-Resolution](https://mlsanity.com/paper/2512.14061/) (2025)
- [Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2303.14324/) (2023)
- [PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-Resolution](https://mlsanity.com/paper/2405.17158/) (2024)
- [Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation](https://mlsanity.com/paper/2408.04158/) (2024)
- [LATIS: Lambda Abstraction-based Thermal Image Super-resolution](https://mlsanity.com/paper/2311.12046/) (2023)
- [IRConStyle: Image Restoration Framework Using Contrastive Learning and Style Transfer](https://mlsanity.com/paper/2402.15784/) (2024)
- [LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-Resolution](https://mlsanity.com/paper/2409.03516/) (2024)
- [Enhanced Super-Resolution Training via Mimicked Alignment for Real-World Scenes](https://mlsanity.com/paper/2410.05410/) (2024)
- [FIPER: Factorized Features for Robust Image Super-Resolution and Compression](https://mlsanity.com/paper/2410.18083/) (2024)
- [StatsMerging: Statistics-Guided Model Merging via Task-Specific Teacher Distillation](https://mlsanity.com/paper/2506.04567/) (2025)

## Abstract (arXiv)

Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation. In contrast to previous SR models that simply stack multiple small kernel convolutions or complex operators to learn representations, we explore a large kernel ConvNet for mobile-friendly SR design. Specifically, we develop a large depth-wise convolution and two projection layers based on channel splitting and shuffling as the basic component to mix features efficiently. Since the contexts of natural images are strongly locally correlated, using large depth-wise convolutions only is insufficient to reconstruct fine details. To overcome this problem while maintaining the efficiency of the proposed module, we introduce Fused-MBConvs into the proposed network to model the local connectivity of different features. Experimental results demonstrate that the proposed ShuffleMixer is about 6x smaller than the state-of-the-art methods in terms of model parameters and FLOPs while achieving competitive performance. In NTIRE 2022, our primary method won the model complexity track of the Efficient Super-Resolution Challenge [23]. The code is available at https://github.com/sunny2109/MobileSR-NTIRE2022.