# AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions

arXiv: https://arxiv.org/abs/2305.03387 · HTML: https://mlsanity.com/paper/2305.03387/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 52 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 30 of 36)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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 Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [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)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [SkipNet: Learning Dynamic Routing in Convolutional Networks](https://mlsanity.com/paper/1711.09485/) (2017)
- [CondConv: Conditionally Parameterized Convolutions for Efficient Inference](https://mlsanity.com/paper/1904.04971/) (2019)
- [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)
- [Adaptive Neural Networks for Efficient Inference](https://mlsanity.com/paper/1702.07811/) (2017)
- [Burst Denoising with Kernel Prediction Networks](https://mlsanity.com/paper/1712.02327/) (2017)
- [Residual Feature Distillation Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2009.11551/) (2020)
- [ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic](https://mlsanity.com/paper/2103.04039/) (2021)
- [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://mlsanity.com/paper/2209.11345/) (2022)
- [Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations](https://mlsanity.com/paper/2004.06965/) (2020)
- [Residual Local Feature Network for Efficient Super-Resolution](https://mlsanity.com/paper/2205.07514/) (2022)
- [Dynamic Video Segmentation Network](https://mlsanity.com/paper/1804.00931/) (2018)
- [Dynamic Region-Aware Convolution](https://mlsanity.com/paper/2003.12243/) (2020)
- [DynamoNet: Dynamic Action and Motion Network](https://mlsanity.com/paper/1904.11407/) (2019)
- [NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results](https://mlsanity.com/paper/2205.05675/) (2022)
- [DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks](https://mlsanity.com/paper/2004.10694/) (2020)
- [Re-parameterizing Your Optimizers rather than Architectures](https://mlsanity.com/paper/2205.15242/) (2022)
- [Dynamic Sampling Networks for Efficient Action Recognition in Videos](https://mlsanity.com/paper/2006.15560/) (2020)
- [Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report](https://mlsanity.com/paper/2105.07825/) (2021)
- [Deciding How to Decide: Dynamic Routing in Artificial Neural Networks](https://mlsanity.com/paper/1703.06217/) (2017)
- [A Comprehensive Benchmark for Single Image Compression Artifacts Reduction](https://mlsanity.com/paper/1909.03647/) (2019)

## Built on by (most cited first, 8 of 8)

- [Compressing Deep Image Super-resolution Models](https://mlsanity.com/paper/2401.00523/) (2023)
- [MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices](https://mlsanity.com/paper/2308.12494/) (2023)
- [I3Net: Inter-Intra-slice Interpolation Network for Medical Slice Synthesis](https://mlsanity.com/paper/2405.02857/) (2024)
- [Your Super Resolution Model is not Enough for Tackling Real-World Scenarios](https://mlsanity.com/paper/2509.06387/) (2025)
- [Compressed Domain Prior-Guided Video Super-Resolution for Cloud Gaming Content](https://mlsanity.com/paper/2501.01773/) (2025)
- [How to warm-start your unfolding network](https://mlsanity.com/paper/2502.01854/) (2025)
- [SCENE: Semantic-aware Codec Enhancement with Neural Embeddings](https://mlsanity.com/paper/2601.22189/) (2026)
- [Exploring Real-Time Super-Resolution: Benchmarking and Fine-Tuning for Streaming Content](https://mlsanity.com/paper/2602.11339/) (2026)

## Abstract (arXiv)

In recent years, videos and images in 720p (HD), 1080p (FHD) and 4K (UHD) resolution have become more popular for display devices such as TVs, mobile phones and VR. However, these high resolution images cannot achieve the expected visual effect due to the limitation of the internet bandwidth, and bring a great challenge for super-resolution networks to achieve real-time performance. Following this challenge, we explore multiple efficient network designs, such as pixel-unshuffle, repeat upscaling, and local skip connection removal, and propose a fast and lightweight super-resolution network. Furthermore, by analyzing the applications of the idea of divide-and-conquer in super-resolution, we propose assembled convolutions which can adapt convolution kernels according to the input features. Experiments suggest that our method outperforms all the state-of-the-art efficient super-resolution models, and achieves optimal results in terms of runtime and quality. In addition, our method also wins the first place in NTIRE 2023 Real-Time Super-Resolution - Track 1 (×2). The code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/AsConvSR