论文阅读目录<1>

序号 论文名称 模型简称
1 Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising DnCNN
2 FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising FFDNet
3 U-Net: Convolutional Networks for Biomedical Image Segmentation U-Net
4 Toward Convolutional Blind Denoising of Real Photographs CBDNet
5 FRACTALNET:ULTRA-DEEP NEURAL NETWORKS WITHOUT RESIDUALS FractalNet
6 Multi-Level Wavelet Convolutional Neural Networks MWCNN
7 HT-MWResnet: Hierarchical Trained and Multi-level Wavelet-Resnet for Image Denoising HT-MWResnet
8 Self-Guided Network for Fast Image Denoising SGN
9 Deep Iterative Down-Up CNN for Image Denoising DIDN
10 Attention-guided CNN for Image Denoising ADNet
11 Image denoising using deep CNN with batch renormalization BRDNet
12 ADRN: ATTENTION-BASED DEEP RESIDUAL NETWORK FOR HYPERSPECTRAL IMAGE DENOISING ADRN
13 Research on Denoising Method of Remote Sensing Images Based on Convolutional Neural Network  
14 Learning Deep CNN Denoiser Prior for Image Restoration  
15 A High-Quality Denoising Dataset for Smartphone Cameras  
16 Real Image Denoising with Feature Attention  
17 基于深度反卷积神经网络的图像超分辨率算法  
18 Learning Enriched Features for Real Image Restoration and Enhancement MIRNet
19 Densely Connected Hierarchical Network for Image Denoising DHDN
20 RandAugment: Practical automated data augmentation with a reduced search space  
21 Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements  
22 Image Super-Resolution Using Very Deep Residual Channel Attention Networks RCAN
23 RIDNet: Recursive Information Distillation Network for Color Image Denoising RIDNet
24 Selective Kernel Networks SKNet
25 Squeeze-and-Excitation Networks SENet
26 Deep Adaptive Inference Networks for Single Image Super-Resolution AdaDSR
27 Enhanced Deep Residual Networks for Single Image Super-Resolution EDSR
28 Pyramid Attention Networks for Image Restoration PANet
29 Pyramid Feature Attention Network for Saliency detection  

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转载自blog.csdn.net/LiuJiuXiaoShiTou/article/details/106282800