# Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-Resolution

arXiv: https://arxiv.org/abs/2501.15774 · HTML: https://mlsanity.com/paper/2501.15774/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2025.

## Built on by (most cited first, 9 of 9)

- [The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2604.03198/) (2026)
- [C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales](https://mlsanity.com/paper/2503.13740/) (2025)
- [Emulating Self-attention with Convolution for Efficient Image Super-Resolution](https://mlsanity.com/paper/2503.06671/) (2025)
- [Large Kernel Modulation Network for Efficient Image Super-Resolution](https://mlsanity.com/paper/2508.11893/) (2025)
- [Deep Spectral Prior](https://mlsanity.com/paper/2505.19873/) (2025)
- [Rank-Factorized Implicit Neural Bias: Scaling Super-Resolution Transformer with FlashAttention](https://mlsanity.com/paper/2603.06738/) (2026)
- [A Cosine Network for Image Super-Resolution](https://mlsanity.com/paper/2601.16413/) (2026)
- [UCAN: Unified Convolutional Attention Network for Expansive Receptive Fields in Lightweight Super-Resolution](https://mlsanity.com/paper/2603.11680/) (2026)
- [DPAMixerSR: An Efficient Degradation-Pattern-Aware Model for Image Super-Resolution](https://mlsanity.com/paper/2609.32705/) (2026)

## Abstract (arXiv)

Transformer-based Super-Resolution (SR) methods have demonstrated superior performance compared to convolutional neural network (CNN)-based SR approaches due to their capability to capture long-range dependencies. However, their high computational complexity necessitates the development of lightweight approaches for practical use. To address this challenge, we propose the Attention-Sharing Information Distillation (ASID) network, a lightweight SR network that integrates attention-sharing and an information distillation structure specifically designed for Transformer-based SR methods. We modify the information distillation scheme, originally designed for efficient CNN operations, to reduce the computational load of stacked self-attention layers, effectively addressing the efficiency bottleneck. Additionally, we introduce attention-sharing across blocks to further minimize the computational cost of self-attention operations. By combining these strategies, ASID achieves competitive performance with existing SR methods while requiring only around 300K parameters - significantly fewer than existing CNN-based and Transformer-based SR models. Furthermore, ASID outperforms state-of-the-art SR methods when the number of parameters is matched, demonstrating its efficiency and effectiveness. The code and supplementary material are available on the project page.