# Lightweight Image Super-Resolution with Adaptive Weighted Learning Network

arXiv: https://arxiv.org/abs/1904.02358 · HTML: https://mlsanity.com/paper/1904.02358/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 16 of 16)

- [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)
- [Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer](https://mlsanity.com/paper/2204.13286/) (2022)
- [Attention in Attention Network for Image Super-Resolution](https://mlsanity.com/paper/2104.09497/) (2021)
- [A Systematic Survey of Deep Learning-based Single-Image Super-Resolution](https://mlsanity.com/paper/2109.14335/) (2021)
- [A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution](https://mlsanity.com/paper/2112.04488/) (2021)
- [Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2207.02796/) (2022)
- [CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2503.06896/) (2025)
- [Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network](https://mlsanity.com/paper/2212.14181/) (2022)
- [Knowledge Distillation in Vision Transformers: A Critical Review](https://mlsanity.com/paper/2302.02108/) (2023)
- [Fully 1×1 Convolutional Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2307.16140/) (2023)
- [Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2503.16056/) (2025)
- [BAM: A Balanced Attention Mechanism for Single Image Super Resolution](https://mlsanity.com/paper/2104.07566/) (2021)
- [A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU](https://mlsanity.com/paper/2303.08999/) (2023)
- [Lightweight single-image super-resolution network based on dual paths](https://mlsanity.com/paper/2409.06590/) (2024)
- [Crafting Query-Aware Selective Attention for Single Image Super-Resolution](https://mlsanity.com/paper/2504.06634/) (2025)

## Abstract (arXiv)

Deep learning has been successfully applied to the single-image super-resolution (SISR) task with great performance in recent years. However, most convolutional neural network based SR models require heavy computation, which limit their real-world applications. In this work, a lightweight SR network, named Adaptive Weighted Super-Resolution Network (AWSRN), is proposed for SISR to address this issue. A novel local fusion block (LFB) is designed in AWSRN for efficient residual learning, which consists of stacked adaptive weighted residual units (AWRU) and a local residual fusion unit (LRFU). Moreover, an adaptive weighted multi-scale (AWMS) module is proposed to make full use of features in reconstruction layer. AWMS consists of several different scale convolutions, and the redundancy scale branch can be removed according to the contribution of adaptive weights in AWMS for lightweight network. The experimental results on the commonly used datasets show that the proposed lightweight AWSRN achieves superior performance on x2, x3, x4, and x8 scale factors to state-of-the-art methods with similar parameters and computational overhead. Code is avaliable at: https://github.com/ChaofWang/AWSRN