Noise2Noise

Title

Noise2Noise: Learning Image Restoration without Clean Data

Abstract

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption. In practice, we show that a single model learns photographic noise removal, denoising synthetic Monte Carlo images, and reconstruction of undersampled MRI scans -- all corrupted by different processes -- based on noisy data only.

Train

$ python main.py --mode train \
                 --scope [scope name] \
                 --name_data [data name] \
                 --dir_data [data directory] \
                 --dir_log [log directory] \
                 --dir_checkpoint [checkpoint directory]
                 --gpu_ids [gpu id; '-1': no gpu, '0, 1, ..., N-1': gpus]

---

$ python main.py --mode train \
                 --scope resnet \
                 --name_data bsd500 \
                 --dir_data ./datasets \
                 --dir_log ./log \
                 --dir_checkpoint ./checkpoint
                 --gpu_ids 0

• Set [scope name] uniquely.
• To understand hierarchy of directories based on their arguments, see directories structure below.
• Hyperparameters were written to arg.txt under the [log directory].

Test

$ python main.py --mode test \
                 --scope [scope name] \
                 --name_data [data name] \
                 --dir_data [data directory] \
                 --dir_log [log directory] \
                 --dir_checkpoint [checkpoint directory] \
                 --dir_result [result directory]
                 --gpu_ids [gpu id; '-1': no gpu, '0, 1, ..., N-1': gpus]

---

$ python main.py --mode test \
                 --scope resnet \
                 --name_data bsd500 \
                 --dir_data ./datasets \
                 --dir_log ./log \
                 --dir_checkpoint ./checkpoints \
                 --dir_result ./results
                 --gpu_ids 0

• To test using trained network, set [scope name] defined in the train phase.
• Generated images are saved in the images subfolder along with [result directory] folder.
• index.html is also generated to display the generated images.

Tensorboard

$ tensorboard --logdir [log directory]/[scope name]/[data name] \
              --port [(optional) 4 digit port number]

---

$ tensorboard --logdir ./log/resnet/bsd500 \
              --port 6006

After the above comment executes, go http://localhost:6006

• You can change [(optional) 4 digit port number].
• Default 4 digit port number is 6006.