# Edge-enhanced Feature Distillation Network for Efficient Super-Resolution arXiv: https://arxiv.org/abs/2204.08759 ยท HTML: https://mlsanity.com/paper/2204.08759/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation. - Builds on 27 of its 46 references that are in the map. - First posted on arXiv 19 Apr 2022. ## Placed in - Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/) ## Builds on (most cited first, 27 of 27) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017) - [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018) - [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014) - [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015) - [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018) - [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021) - [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016) - [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015) - [Single Image Super-Resolution via a Holistic Attention Network](https://mlsanity.com/paper/2008.08767/) (2020) - [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) - [ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks](https://mlsanity.com/paper/1908.03930/) (2019) - [Residual Feature Distillation Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2009.11551/) (2020) - [Fast and Accurate Single Image Super-Resolution via Information Distillation Network](https://mlsanity.com/paper/1803.09454/) (2018) - [Efficient Image Super-Resolution Using Pixel Attention](https://mlsanity.com/paper/2010.01073/) (2020) - [Diverse Branch Block: Building a Convolution as an Inception-like Unit](https://mlsanity.com/paper/2103.13425/) (2021) - [Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search](https://mlsanity.com/paper/1901.07261/) (2019) - [Learning Filter Basis for Convolutional Neural Network Compression](https://mlsanity.com/paper/1908.08932/) (2019) - [DHP: Differentiable Meta Pruning via HyperNetworks](https://mlsanity.com/paper/2003.13683/) (2020) - [Efficient Residual Dense Block Search for Image Super-Resolution](https://mlsanity.com/paper/1909.11409/) (2019) - [AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results](https://mlsanity.com/paper/2009.06943/) (2020) - [Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution](https://mlsanity.com/paper/2101.06658/) (2021) - [The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures](https://mlsanity.com/paper/2006.16242/) (2020) - [Gradient Variance Loss for Structure-Enhanced Image Super-Resolution](https://mlsanity.com/paper/2202.00997/) (2022) ## Built on by (most cited first, 18 of 18) - [The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2404.10343/) (2024) - [CAMixerSR: Only Details Need More "Attention"](https://mlsanity.com/paper/2402.19289/) (2024) - [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) - [DVMSR: Distillated Vision Mamba for Efficient Super-Resolution](https://mlsanity.com/paper/2405.03008/) (2024) - [DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution](https://mlsanity.com/paper/2304.07018/) (2023) - [Tele-Aloha: A Low-budget and High-authenticity Telepresence System Using Sparse RGB Cameras](https://mlsanity.com/paper/2405.14866/) (2024) - [AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions](https://mlsanity.com/paper/2305.03387/) (2023) - [Outlier-Aware Post-Training Quantization for Image Super-Resolution](https://mlsanity.com/paper/2511.00682/) (2025) - [Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem](https://mlsanity.com/paper/2409.03179/) (2024) - [Residual Decoder Adapter: ID-Preserving Tokenizer Adaption for Autoregressive Text Rendering](https://mlsanity.com/paper/2606.01911/) (2026) - [Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution](https://mlsanity.com/paper/2305.17898/) (2023) - [HASN: Hybrid Attention Separable Network for Efficient Image Super-resolution](https://mlsanity.com/paper/2410.09844/) (2024) - [Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction](https://mlsanity.com/paper/2209.08337/) (2022) - [Towards Accurate Post-training Quantization for Reparameterized Models](https://mlsanity.com/paper/2402.16121/) (2024) - [Enhancing Frequency for Single Image Super-Resolution with Learnable Separable Kernels](https://mlsanity.com/paper/2506.04555/) (2025) - [Efficient Perceptual Image Super Resolution: AIM 2025 Study and Benchmark](https://mlsanity.com/paper/2510.12765/) (2025) - [No Calibration, No Depth, No Problem: Cross-Sensor View Synthesis with 3D Consistency](https://mlsanity.com/paper/2602.23559/) (2026) ## Abstract (arXiv) With the recently massive development in convolution neural networks, numerous lightweight CNN-based image super-resolution methods have been proposed for practical deployments on edge devices. However, most existing methods focus on one specific aspect: network or loss design, which leads to the difficulty of minimizing the model size. To address the issue, we conclude block devising, architecture searching, and loss design to obtain a more efficient SR structure. In this paper, we proposed an edge-enhanced feature distillation network, named EFDN, to preserve the high-frequency information under constrained resources. In detail, we build an edge-enhanced convolution block based on the existing reparameterization methods. Meanwhile, we propose edge-enhanced gradient loss to calibrate the reparameterized path training. Experimental results show that our edge-enhanced strategies preserve the edge and significantly improve the final restoration quality. Code is available at https://github.com/icandle/EFDN.