图像超分辨率IDN的Pytorch复现代码，注释详细，含科研绘图，最优SSIM和PSNR的模型权重文件(x2、x3、x4)

# IDN This repository is implementation of the "Fast and Accurate Single Image Super-Resolution via Information Distillation Network". <center><img src="./figs/fig2.png"></center> <center><img src="./figs/fig3.png"></center> ## Requirements - PyTorch - Tensorflow - tqdm - Numpy - Pillow **Tensorflow** is required for quickly fetching image in training phase. ## Results <table> <tr> <td><center>Original</center></td> <td><center>BICUBIC x2</center></td> <td><center>IDN x2</center></td> </tr> <tr> <td> <center><img src="./data/comic.bmp""></center> </td> <td> <center><img src="./data/comic_x2_bicubic.png"></center> </td> <td> <center><img src="./data/comic_x2_IDN.png"></center> </td> </tr> </table> ## Usages ### Train When training begins, the model weights will be saved every epoch. <br /> If you want to train quickly, you should use **--use_fast_loader** option. ```bash python main.py --scale 2 \ --num_features 64 \ --d 16 \ --s 4 \ --images_dir "" \ --outputs_dir "" \ --patch_size 29 \ --batch_size 16 \ --num_epochs 20 \ --lr 1e-4 \ --loss "l1" \ --threads 8 \ --seed 123 \ --use_fast_loader ``` ### Fine-tuning The fine-tuning artifacts are generated as "IDN_ft_epoch_{}.pth". ```bash python main.py --scale 2 \ --num_features 64 \ --d 16 \ --s 4 \ --images_dir "" \ --outputs_dir "" \ --weights_path "" \ --patch_size 39 \ --batch_size 16 \ --num_epochs 20 \ --lr 1e-5 \ --loss "l2" \ --threads 8 \ --seed 123 \ --use_fast_loader ``` ### Test Output results consist of restored images by the BICUBIC and the IDN. ```bash python example --scale 2 \ --num_features 64 \ --d 16 \ --s 4 \ --weights_path "" \ --image_path "" \ --outputs_dir "" ```