# Fast and Accurate Single Image Super-Resolution via Information Distillation Network

arXiv: https://arxiv.org/abs/1803.09454 · HTML: https://mlsanity.com/paper/1803.09454/

## Key facts

- Cited by 121 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2018.

## Built on by (most cited first, 30 of 121)

- [Transformer for Single Image Super-Resolution](https://mlsanity.com/paper/2108.11084/) (2021)
- [Learning Enriched Features for Fast Image Restoration and Enhancement](https://mlsanity.com/paper/2205.01649/) (2022)
- [Burst Image Restoration and Enhancement](https://mlsanity.com/paper/2110.03680/) (2021)
- [Omni Aggregation Networks for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2304.10244/) (2023)
- [Residual Local Feature Network for Efficient Super-Resolution](https://mlsanity.com/paper/2205.07514/) (2022)
- [Blueprint Separable Residual Network for Efficient Image Super-Resolution](https://mlsanity.com/paper/2205.05996/) (2022)
- [NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results](https://mlsanity.com/paper/2205.05675/) (2022)
- [Learning Frequency-aware Dynamic Network for Efficient Super-Resolution](https://mlsanity.com/paper/2103.08357/) (2021)
- [Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search](https://mlsanity.com/paper/2108.08910/) (2021)
- [CAMixerSR: Only Details Need More "Attention"](https://mlsanity.com/paper/2402.19289/) (2024)
- [Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2112.08655/) (2021)
- [Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution](https://mlsanity.com/paper/2204.08397/) (2022)
- [CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution](https://mlsanity.com/paper/2207.10345/) (2022)
- [The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2504.10686/) (2025)
- [Anchor-based Plain Net for Mobile Image Super-Resolution](https://mlsanity.com/paper/2105.09750/) (2021)
- [DVMSR: Distillated Vision Mamba for Efficient Super-Resolution](https://mlsanity.com/paper/2405.03008/) (2024)
- [A Comprehensive Review of Deep Learning-based Single Image Super-resolution](https://mlsanity.com/paper/2102.09351/) (2021)
- [Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution](https://mlsanity.com/paper/2203.07682/) (2022)
- [Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer](https://mlsanity.com/paper/2204.13286/) (2022)
- [Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances](https://mlsanity.com/paper/2209.13131/) (2022)
- [A Two-Stage Attentive Network for Single Image Super-Resolution](https://mlsanity.com/paper/2104.10488/) (2021)
- [Self-Calibrated Efficient Transformer for Lightweight Super-Resolution](https://mlsanity.com/paper/2204.08913/) (2022)
- [Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution](https://mlsanity.com/paper/2207.12577/) (2022)
- [Collapsible Linear Blocks for Super-Efficient Super Resolution](https://mlsanity.com/paper/2103.09404/) (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)
- [RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization](https://mlsanity.com/paper/2205.05671/) (2022)
- [DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution](https://mlsanity.com/paper/2304.07018/) (2023)
- [Self-Regulation for Semantic Segmentation](https://mlsanity.com/paper/2108.09702/) (2021)
- [Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2203.08921/) (2022)

## Abstract (arXiv)

Recently, deep convolutional neural networks (CNNs) have been demonstrated remarkable progress on single image super-resolution. However, as the depth and width of the networks increase, CNN-based super-resolution methods have been faced with the challenges of computational complexity and memory consumption in practice. In order to solve the above questions, we propose a deep but compact convolutional network to directly reconstruct the high resolution image from the original low resolution image. In general, the proposed model consists of three parts, which are feature extraction block, stacked information distillation blocks and reconstruction block respectively. By combining an enhancement unit with a compression unit into a distillation block, the local long and short-path features can be effectively extracted. Specifically, the proposed enhancement unit mixes together two different types of features and the compression unit distills more useful information for the sequential blocks. In addition, the proposed network has the advantage of fast execution due to the comparatively few numbers of filters per layer and the use of group convolution. Experimental results demonstrate that the proposed method is superior to the state-of-the-art methods, especially in terms of time performance.