网址：https://zte.hina.com/zte/denoise
无缘复赛，太菜了，不好意思说自己学去噪的了，代码会开源，但是感觉没什么人看吧
Attention Mechanisms in Computer Vision: A Survey
https://arxiv.org/abs/2111.07624
通道：
Squeeze-and excitation networks
MPRNet用了：Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Ecanet: Efficient channel attention for deep convolutional neural networks
Context encoding for semantic segmentation
Global secondorder pooling convolutional networks
Srm : A style-based recalibration module for convolutional neural networks
Gated channel transformation for visual recognition
Fcanet: Frequency channel attention networks
Spatio-temporal channel correlation networks for action classification
You look twice: Gaternet for dynamic filter selection in cnns
Spsequencenet: Semantic segmentation network on 4d point clouds
空间：
70篇非常多
通道-空间：
19篇非常多，列举重要的
CBAM: convolutional block attention module，训练太慢了，停止
Dual attention network for scene segmentation，爆显存
SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning
Bam: Bottleneck attention module，很多错误
Simam: A simple, parameter-free attention module for convolutional neural networks
Coordinate attention for efficientmobile network design，可以训练，很差
SA-Net: Shuffle Attention for Deep Convolutional Neural Networks，可以训练，很差
MIRNet中的DAU，可以训练，很差
损失函数：L2+L1+FFT(尝试中)，MSSSIM，NAFNet中的PSNRloss
去模糊的网络：DeepRFT(signal number 32报错)，mimonet
加入跳跃连接
NAFNet改参数([4,8] 8 [4,8]随机切图旋转)
unet+mixup
GAN的使用
第17名：https://github.com/Fivethousand5k/ZTE_RawDenoise
第58名：https://blog.csdn.net/weixin_44875593/article/details/124909747
