说明：除了超分领域的论文外，同时也会附加一些backbone网络或者building block、去噪（Denoise）、去雾（Dehaze）、去雨（Derain）去模糊（Deblur）、修复（Restoration）等对超分领域有启发的相关领域论文！我自己是一枚科研小白，熬肝整理，希望能给大家一些帮助，如果各路大神有比较好的见解、相关资料等等，欢迎在评论区补充，多多交流！
标题来源&时间方向源码状态推荐指数相关资料
Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural NetworksECCV 2020注意力机制无未读8.5与SENet互补提升，华为诺亚提出自注意力新机制：Weight Excitation时间方向源√分数资料MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without TricksarXiv 2020知识蒸馏MEAL-V2未读分数无需额外数据、Tricks、架构调整，CMU开源首个将ResNet50精度提升至80%+新方法Role of Orthogonality Constraints in Improving Properties of Deep Networks for Image Classification2020.09.22模型鲁棒性无未读8【正交球面正则化】让模型不偏不倚更加鲁棒的简单粗暴神器，推荐阅读和使用！！！Learning Image-adaptive 3D Lookup Tables for High Performance Photo enhancement in Real-time2020图像增强3DLUT未读8图像增强领域大突破！以1.66ms的速度处理4K图像，港理工提出图像自适应的3DLUTLearning a Single Convolutional Super-Resolution Network for Multiple DegradationsCVPR 2018适用于多种退化模型的单个超分模型有√8.51，2Spatial Transformer NetworksNIPS 2015平移不变性torch未读8详细解读Spatial Transformer Networks（STN）EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksICML 2019网络架构设计PyTorch Tensorflow√8.5简短的翻译Image Super-Resolution via Dual-State Recurrent NetworksCVPR 2018双状态递归超分DSRN√8相关资料Multi-scale Residual Network for Image Super-ResolutionECCV 2018适用于多个scale的单个超分模型MSRN√8.5Invertible Image RescalingECCV 2020可逆的图像缩放、高低频信息的分离和利用IRN√8.5ECCV 2020 Oral·可逆图像缩放：完美恢复降采样后的高清图片Single Image Super-Resolution via a Holistic Attention NetworkECCV 2020整体注意力机制无√9ECCV2020最新图像超分辨重建文章A Deep Journey into Super-resolution：A SurveyACM Computing Surveys 2020CNN-based SISR综述无√8.5资料Brief Survey of Single Image Super-Resolution Reconstruction Based on Deep Learning Approaches2020CNN-based SISR综述无√8.5资料Deep Learning for Image Super-resolution：A SurveyIEEE TPAMI 2020CNN-based SISR综述无√8.5资料A Survey of Image Super Resolution Based on CNN2020CNN-based SISR综述无√8.5资料Deep Learning for Image Super-resolution：A Survey2020CNN-based SISR综述无√8.5资料Deep Learning Based Single Image Super-resolution：A Survey2019CNN-based SISR综述无√8.5资料Deep learning for single image super-resolution：A brief review2019CNN-based SISR综述无√8.5资料ResNeSt- Split-Attention Networks2020网络架构设计ResNeSt√8【论文笔记】张航和李沐等提出：ResNeSt: Split-Attention Networks（ResNet改进版本）Single Image Super-Resolution via Residual Neuron Attention Networks2020RNAN、注意力无√8.5资料Learning with Privileged Information for Efficient Image Super-ResolutionECCV 2020PISR、基于知识蒸馏的超分PISR√8[ECCV2020] PISR - 把蒸馏Distillation成功应用到超分任务Dynamic Convolutions- Exploiting Spatial Sparsity for Faster InferenceCVPR 2020动态卷积dynconv√8资料Rethinking Data Augmentation for Image Super-resolution- A Comprehensive Analysis and a New StrategyCVPR 2020专用与超分领域的DAcutblur√8.5资料Learning Texture Transformer Network for Image Super-ResolutionCVPR 2020TTSR、纹理迁移TTSR√8资料Image Super-Resolution With Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars MiningCVPR 2020CL-NL注意力CL-NL√8.5资料Explorable Super ResolutionCVPR 2020超分开源√7.8资料Residual Feature Aggregation Network for Image Super-ResolutionCVPR 2020RFANet、空间域注意力无√8.5资料Channel Attention Based Iterative Residual Learning for Depth Map Super-ResolutionCVPR 2020DSR、通道域注意力、深度图超分无√8.5资料Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionarXiv 2020迭代残差无√8资料Attention Cube Network for Image RestorationACM MM 2020A-CubeNet、立体注意力机制无√8.5资料Gcnet- Non-local networks meet squeeze-excitation networks and beyondICCVW 2019Gcnet、NL注意力GCNet√8GCNet：当Non-local遇见SENetFeedback Network for Image Super-ResolutionCVPR 2019SRFBN、反馈机制SRFBN√8.5资料SRFBN的PyTorch实现Selective Kernel NetworksCVPR 2019SKNet、注意力SKNet√7.9资料Second-order attention network for single image super-resolutionCVPR 2019SAN、二阶注意力机制SAN√8资料Residual Non-local Attention Networks for Image RestorationICLR 2019RNAN、NL注意力、图像修复无√8.5资料Residual Non-local Attention Networks for Image RestorationNASNet- A Neuron Attention Stage-by-Stage Net for Single Image Deraining2019NASNet、多阶段神经元注意力机制、去雨无√8.5资料Lightweight Image Super-Resolution with Information Multi-distillation NetworkACM MM 2019IMDN、通道分裂、轻量IMDN√8资料Hybrid Residual Attention Network for Single Image Super ResolutionIEEE Access 2019HRAN、残差、注意力HRAN√8资料DANet- Dual Attention Network for Scene Segmentation2019 CVPRDANet、注意力、语义分割DANet√8资料Efficient Single Image Super-Resolution via Hybrid Residual Feature Learning with Compact Back-Projection NetworkCVPRW 2019CBPN、反向投影无√7无Bag of Tricks for Image Classification with Convolutional Neural Networks2018训练技巧Bag of Tricks√8.5【模型性能杀器解读】如果项目的模型遇到瓶颈，用这些Tricks就对了！！！Wide Activation for Efficient and Accurate Image Super-Resolution2018WDSR、宽、残差WDSR√8.5资料Residual dense network for image super-resolutionCVPR 2018RDN、残差、稠密连接RDN√8资料Image Super-Resolution Using Very Deep Residual Channel Attention NetworksECCV 2018RCAN、通道注意力RCAN√8.5资料RAM- Residual Attention Module for Single Image Super-Resolution2018RAM、注意力无√8.5资料PSANet：Point-wise Spatial Attention Network for Scene ParsingECCV 2018PSANet、空间注意力PSANet√8资料Non-local recurrent network for image restorationNIPS 2018NLRN、NL注意力NLRN√8.5资料Non-local Neural Networks2018NLNet、NL注意力NLNet√8.5资料Fast and accurate image super-resolution with deep laplacian pyramid networks2018MS-LapSRN、拉普拉斯金字塔MS-LapSRN√8资料Fast and Accurate Single Image Super-Resolution via Information Distillation NetworkCVPR 2018IDN、轻量IDN√8资料Deep back-projection networks for super-resolutionCVPR 2018D-DBPN、反向投影D-DBPN√8.5资料Deep Back-Projection Networks for Single Image Super-resolution2020方向源√分数资料Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution2018CSAR、注意力无√8.5资料CBAM- Convolutional Block Attention ModuleECCV 2018CBAM、注意力CBAM√9资料Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual NetworkECCV 2018CARN、级联、轻量CARN√8资料BAM- Bottleneck Attention ModuleBMVC 2018BAM、注意力BAM√8.5资料A^2-Nets- Double Attention NetworksNIPS 2018A^2-Nets、双注意力A2-Nets√8【文献阅读01】A2-Nets: Double Attention Networks、 【文献阅读02】A2-Nets: Double Attention Networks时间方向源√分数资料
Processed:
0.024, SQL:
8
