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| ## Prerequisites | ||
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| 1. Refer to the [INSTALL.md](../INSTALL.md) for instructions on preparing environment and dependencies. | ||
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| 2. Download [training](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155231343_link_cuhk_edu_hk/EUWR-KgxXD5OsH85ylom4H4BPv2hjYSMAyp4MkopiVnqoQ?e=mfcZBX) and [testing](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155231343_link_cuhk_edu_hk/EfIPJHRaH_VGrxJHD7W60ZEBO79Cet6rKSJsbfQGjue75Q?e=OTDAe0) datasets from One Drive. | ||
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| ## Train | ||
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| 1. Put training datasets in `'./datasets/fivek/Raw'` and testing datasets in `'./datasets/DND'`, following: | ||
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| ``` | ||
| datasets/ | ||
| ├── fivek/ | ||
| │ └── list_file/ | ||
| │ ├── invalid_list.txt | ||
| │ ├── train_list.txt | ||
| │ ├── val_list.txt | ||
| │ └── Raw/ | ||
| │ ├── a0001-jmac_DSC1459.dng | ||
| │ ├── a0002-dgw_005.dng | ||
| │ ├── ... | ||
| │ ├── a5000-kme_0204.dng | ||
| | | ||
| ├── DND/ | ||
| │ └── list_file/ | ||
| │ ├── val_list.txt | ||
| │ └── Raw/ | ||
| │ ├── 0001.mat | ||
| │ ├── ... | ||
| │ ├── 0050.mat | ||
| │ ├── info.mat | ||
| │ ├── pixelmasks.mat | ||
| ``` | ||
| 2. Open the `'./options/DualDn_Big.yml'` file, set `gamma_type` in `datasets/val/syn_isp` to `2.2` since we find that DND benchmark generates ground truth images with `x ** 1/2.2` gamma. | ||
| 3. Run | ||
| ``` | ||
| python train_dualdn.py -opt ./options/DualDn_Big.yml | ||
| ``` | ||
| 4. For fast validation, we validate 20 synthetic images instead of the real-captured images every 50,000 iterations, since images in DND benchmark are too many to validate. | ||
| - If you'd like to validate directly on DND benchmark images, open the `DualDn_Big.yml` file, set `mode` in `datasets/val/val_datasets/DND` to `true` and set `mode` in `datasets/val/val_datasets/Synthetic` to `false`. | ||
| - We recommend evaluating on DND benchmark images after training using the following test or inference code. | ||
| 5. Find the training results in `'./experiments'` | ||
| ## Test | ||
| 1. After training, you can test DualDn in various testing sets, here we test DND benchmark images for example. | ||
| 2. Run | ||
| ``` | ||
| python test_dualdn.py -opt [exp_option_path] --num_iters [iters] --val_datasets ['Synthetic', 'Real_captured', 'DND'] | ||
| ``` | ||
| E.g. If you trained DualDn with `'DualDn_Big.yml'` for `300000` iterations, and want to test it on `DND` datasets: | ||
| ``` | ||
| python test_dualdn.py -opt ./experiments/DualDn_Big/DualDn_Big.yml --num_iters 300000 --val_datasets DND | ||
| ``` | ||
| 3. Find the testing results in `'./results'` <br> | ||
| After testing, the file structure should follows: | ||
| ``` | ||
| results/DualDn_Big | ||
| ├── Raw/ | ||
| │ └── bundled/ | ||
| │ ├── 0001.mat | ||
| │ ├── ... | ||
| │ ├── 0050.mat | ||
| │ ├── 0001_01.mat | ||
| │ ├── ... | ||
| │ ├── 0050_20.mat | ||
| | | ||
| ├── sRGB/ | ||
| │ └── bundled/ | ||
| │ ├── 0001.mat | ||
| │ ├── ... | ||
| │ ├── 0050.mat | ||
| │ ├── 0001_01.mat | ||
| │ ├── ... | ||
| │ ├── 0050_20.mat | ||
| | | ||
| ├── visuals/ | ||
| │ ├── 0001_01_ours.png | ||
| │ ├── ... | ||
| │ ├── 0050_20_ours.png | ||
| ``` | ||
| You can download the files in `'Raw/bundled/'` to upload to the [online DND benchmark](https://noise.visinf.tu-darmstadt.de/) for evaluation on the raw-denoising track, and download the files in `'sRGB/bundled/'` for the sRGB-denoising track evaluation. | ||
| For a closer look at DualDn’s visual results, refer to the `'/visuals'` folder. <br> | ||
| - Note that the DND benchmark currently doesn’t support a dual-denoising evaluation track. Additionally, **DND generates its ground truth images using a simplified ISP compared to our DualDn's ISP** (as explained in the Supplementary). | ||
| - We have not yet been able to contact the DND's owner to obtain the original ISP code. <br> | ||
| 🌟 **If the original ISP of DND is given, the PSNR could potentially improve by around 1 dB.** | ||
| ## Inference | ||
| 1. For fast inference, you can use the pre-trained DualDn models to process DND benchmark images. | ||
| 2. Download the [pre-trained model](https://mycuhk-my.sharepoint.com/:u:/g/personal/1155231343_link_cuhk_edu_hk/EeSssinwPSRLvC2zOTdmAd8BLLtF3MaKfFw2kYv25WthkQ?e=bbO0Ql) from One Drive and place it in `'./pretrained_model'` | ||
| 3. Run | ||
| ``` | ||
| python inference_dualdn.py -opt ./options/DualDn_Big.yml --pretrained_model ./pretrained_model/DualDn_Big.pth --val_datasets DND --gamma_type 2.2 | ||
| ``` | ||
| - gamma_type **MUST** be set to `'2.2'` since we find that DND benchmark generates ground truth images with `x ** 1/2.2` gamma. | ||
| 4. Find the inferencing results in `'./results'` <br> | ||
| After inferencing, the file structure should follows: | ||
| ``` | ||
| results/DualDn_Big | ||
| ├── Raw/ | ||
| │ └── bundled/ | ||
| │ ├── 0001.mat | ||
| │ ├── ... | ||
| │ ├── 0050.mat | ||
| │ ├── 0001_01.mat | ||
| │ ├── ... | ||
| │ ├── 0050_20.mat | ||
| | | ||
| ├── sRGB/ | ||
| │ └── bundled/ | ||
| │ ├── 0001.mat | ||
| │ ├── ... | ||
| │ ├── 0050.mat | ||
| │ ├── 0001_01.mat | ||
| │ ├── ... | ||
| │ ├── 0050_20.mat | ||
| | | ||
| ├── visuals/ | ||
| │ ├── 0001_01_ours.png | ||
| │ ├── ... | ||
| │ ├── 0050_20_ours.png | ||
| ``` | ||
| You can download the files in `'Raw/bundled/'` to upload to the [online DND benchmark](https://noise.visinf.tu-darmstadt.de/) for evaluation on the raw-denoising track, and download the files in `'sRGB/bundled/'` for the sRGB-denoising track evaluation. | ||
| For a closer look at DualDn’s visual results, refer to the `'/visuals'` folder. <br> | ||
| - Note that the DND benchmark currently doesn’t support a dual-denoising evaluation track. Additionally, **DND generates its ground truth images using a simplified ISP compared to our DualDn's ISP** (as explained in the Supplementary). | ||
| - We have not yet been able to contact the DND's owner to obtain the original ISP code. <br> | ||
| 🌟 **If the original ISP of DND is given, the PSNR could potentially improve by around 1 dB.** |