BiSR

Official implementation of Bilateral Upsampling Network for Single Image Super-Resolution with Arbitrary Scaling Factors(PyTorch)

Our code is built on EDSR(PyTorch) and Mata-SR.

Requirements

• Pytorch 1.5.0
• Python 3.7
• numpy
• skimage
• imageio
• cv2

Install and run demo

1. download the code

git clone https://github.com/Merle314/BiSR
cd BiSR/src

2. run training demo:

python3 main.py --save RBiRDN_BI --model RBiRDN --epochs 2000 --batch_size 16 --patch_size 50 --save_models --save_results --reset --ext sep --GPU_ids 0,1,2,3 --pre_train ../pretrain/RBiRDN_BI/model/model_latest.pt 

3. run test demo:

• download the model from the BaiduYun fetch code: p70u

python3 main.py --model RBiCARN --ext img --save RBiCARN_BI_Test_Urban100 --GPU_ids 0 --batch_size 1 --test_only --data_test Urban100 --pre_train ../pretrain/BiCARN_BI/model/model_latest.pt --save_results

Train and Test as our paper

1. prepare dataset

• download the dataset DIV2K and test dataset fetch code: ev7u GoogleDrive
• change the path_src = DIV2K HR image folder path and run /prepare_dataset/generate_LR_metasr_X1_X4_idealboy.m
• upload the dataset
• change the dir_data in option.py： dir_data = "/path to your DIV2K and testing dataset'(keep the training and test dataset in the same folder: test dataset under the benchmark folder and training dataset rename to DIV2K, or change the data_train to your folder name)

2. pre_train model for test BaiduYun fetch code: p70u
   GoogleDrive

train

cd BiSR/src 
python3 main.py --save RBiRDN_BI --model RBiRDN --epochs 2000 --batch_size 16 --patch_size 50 --save_models --save_results --reset --ext sep --GPU_ids 0,1,2,3 --pre_train ../pretrain/RBiRDN_BI/model/model_latest.pt 

test

python3 main.py --model RBiCARN --ext img --save RBiCARN_BI_Test_Urban100 --GPU_ids 0 --batch_size 1 --test_only --data_test Urban100 --pre_train ../pretrain/BiCARN_BI/model/model_latest.pt --save_results

Citation