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This repository is a PyTorch version of the paper "Multi-scale Residual Network for Image Super-Resolution" (ECCV 2018).
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| 2026-07-24 | 299 |
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# MSRN_PyTorch ### This repository is an official PyTorch implementation of the paper "Multi-scale Residual Network for Image Super-Resolution". Paper can be download from <a href="http://openaccess.thecvf.com/content_ECCV_2018/papers/Juncheng_Li_Multi-scale_Residual_Network_ECCV_2018_paper.pdf">MSRN</a> All test datasets (Preprocessed HR images) can be downloaded from <a href="https://www.jianguoyun.com/p/DcrVSz0Q19ySBxiTs4oB">here</a>. All original test datasets (HR images) can be downloaded from <a href="https://www.jianguoyun.com/p/DaSU0L4Q19ySBxi_qJAB">here</a>. Our MSRN was trained and tested on the Y channel directly. However, more and more SR models are trained on RGB channels. For a fair comparison, we retrained MSRN based on <a href="https://github.com/thstkdgus35/EDSR-PyTorch">EDSR</a> code. We release the new codes and results on this project. The old codes are moved into the OLD/ folder. The new codes are stored on MSRN/ folder. ### Update 2019.06.12.1 The retraining model provided previously uses the DIV2K (1-895). We corrected this error and provided retrained models (DIV2K 1-800) and results. We also provided x8 results now! Notice that, we only use 800 images (DIV2K 1-800) for training and use the latest weight file for the test. ### Update 2019.06.12.2 All pretrained model can be downloaded from <a href="https://www.jianguoyun.com/p/DQpSSlQQ19ySBxjH2IYB">ECCV2018_MSRN_premodel</a> or can be found "Test/model". All reconstructed images can be downloaded from <a href="https://www.jianguoyun.com/p/DQW60AIQ19ySBxjD8ckB">ECCV2018_MSRN_SR_images</a> （Including MSRN and MSRN+）. #### At the same time, we also noticed the defect of MSRN, the improved version of MSRN will be released soon. -------------------- ## Prerequisites: 1. Python 3.6 2. PyTorch >= 0.4.0 3. numpy 4. skimage 5. imageio 6. matplotlib 7. tqdm For more informaiton, please refer to <a href="https://github.com/thstkdgus35/EDSR-PyTorch">EDSR</a> and <a href="https://github.com/yulunzhang/RCAN">RCAN</a>. ## Document Train/ : all train files Test/ : all test files demo.sh : all running instructions ## Dataset We used DIV2K dataset to train our model. Please download it from <a href="https://data.vision.ee.ethz.ch/cvl/DIV2K/">here</a> or <a href="https://cv.snu.ac.kr/research/EDSR/DIV2K.tar">SNU_CVLab</a>. Extract the file and put it into the Train/dataset. ## Training Using --ext sep_reset argument on your first running. You can skip the decoding part and use saved binaries with --ext sep argument in second time. If you have enough memory, using --ext bin. ``` cd Train/ # MSRN x2 LR: 48 * 48 HR: 96 * 96 python main.py --template MSRN --save MSRN_X2 --scale 2 --reset --save_results --patch_size 96 --ext sep_reset # MSRN x3 LR: 48 * 48 HR: 144 * 144 python main.py --template MSRN --save MSRN_X3 --scale 3 --reset --save_results --patch_size 144 --ext sep_reset # MSRN x4 LR: 48 * 48 HR: 192 * 192 python main.py --template MSRN --save MSRN_X4 --scale 4 --reset --save_results --patch_size 192 --ext sep_reset ``` ## Testing Using pre-trained model for training, all test datasets must be pretreatment by ''Test/Prepare_TestData_HR_LR.m" and all pre-trained model should be put into "Test/model/". ``` #MSRN x2 python main.py --data_test MyImage --scale 2 --model MSRN --pre_train ../model/MSRN_x2.pt --test_only --save_results --chop --save "MSRN" --testpath ../LR/LRBI --testset Set5 #MSRN+ x2 python main.py --data_test MyImage --scale 2 --model MSRN --pre_train ../model/MSRN_x2.pt --test_only --save_results --chop --self_ensemble --save "MSRN_plus" --testpath ../LR/LRBI --testset Set5 #MSRN x3 python main.py --data_test MyImage --scale 3 --model MSRN --pre_train ../model/MSRN_x3.pt --test_only --save_results --chop --save "MSRN" --testpath ../LR/LRBI --testset Set5 #MSRN+ x3 python main.py --data_test MyImage --scale 3 --model MSRN --pre_train ../model
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