GitHub – fqnchina/DecoupleLearning: Implementation codes of ECCV 2018 paper “Decouple Learning for Parameterized Image Operators”

This paper is implemented with Pytorch framework.

Demo

Directly run evaluation_10_operator_model.py to test our model on the trained ten image operators with different parameter settings, such as smoothness strength, corruption level of input images.

Users can also test our models trained on 6 filtering based operators (evaluation_6_filter_operator_model.py), 4 restoration operators (evaluation_4_restoration_operator_model.py) or each single operator with different parameters (evaluation_1_operator_model.py).

Data Generation

Run training_data_generation.m in MATLAB to generate the training or test data for the nine image operators mentioned in the paper, which includes L0 smoothing, RTV, WLS, RGF, WMF, shock filter, super resolution, denoising and deblocking. The training data for the derain task is collected from this paper.

The pre-generated evaluation images used in the paper can be downloaded here.

The generated training or test images need to be collected into a file list with the same form of example_filelist.txt for easy training.

Training

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