Learnlets are a way to learn a filter bank rather than design one like in the curvelets.
This filter bank will be learned in a denoising setting with backpropagation and gradient descent.
The requirements are listed in learning_wavelets/requirements.txt.
The learnlets are defined in learning_wavelets/learnlet_model.py, via the class Learnlet.
You can use different types of thresholding listed in learning_wavelets/keras_utils/thresholding.py.
| Model id | Params |
|---|---|
| learnlet_dynamic_st_bsd500_0_55_1580806694 | the big classical network, with 256 filters + identity |
| learnlet_subclassing_st_bsd500_0_55_1582195807 | 64 filters, subclassed API, exact recon forced |
| Model id | Params |
|---|---|
| learnlet_dynamic_st_bsd500_0_55_1580806694 | the big classical network, with 256 filters + identity |
| Model id | Params |
|---|---|
| learnlet_dynamic_st_bsd500_0_55_1580806694 | the big classical network, with 256 filters + identity |
| learnlet_dynamic_st_bsd500_20_40_1580492805 | same with training on 20;40 noise std |
| learnlet_dynamic_st_bsd500_30_1580668579 | same with training on 30 noise std |
| unet_dynamic_st_bsd500_0_55_1576668365 | big classical unet with 64 base filters and batch norm |
| unet_dynamic_st_bsd500_20.0_40.0_1581002329 | same with training on 20;40 noise std |
| unet_dynamic_st_bsd500_30.0_30.0_1581002329 | same with training on 30 noise std |
| Model id | Params |
|---|---|
| learnlet_dynamic_st_bsd500_0_55_1580806694 | the big classical network, with 256 filters + identity |
| unet_dynamic_st_bsd500_0_55_1576668365 | big classical unet with 64 base filters and batch norm |