This repo is for OpenMMLab Algorithm Ecological Challenge, and paper is Unfolding the Alternating Optimization for blind super resolution
We add some codes based on openmmlab/mmediting. You have two methods to train:
- pre-generate all training set by using our preprocess_div2k_dataset.py and use our dataset class bsr_folder_dataset.py
- generate low-quality images and kernels during training by defining the training pipeline in configuration file. We add a degradation class in augmentaion.py
You can just use two config files:
- dan_div2k_x4_gt_only.py. Remember replace
augmentation.py
with ours in mmediting - DAN_DIV2K_x4_v2.py.
Remember to generate PCA encoder by running create_pca_encoder.py
Then, training command is the same as openmmlab/mmediting
The evaluation for two methods are the same because we define same pipelines. Just be careful with datasetloader we use.
Model weights: Baidu:eqp3