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HGGT

Official PyTorch code and dataset for our paper "HGGT" in CVPR 2023.

Paper | Supplementary | Arxiv Version

Human Guided Ground-truth Generation for Realistic Image Super-resolution <br> Du CHEN*, Jie LIANG*, Xindong ZHANG, Ming LIU, Hui ZENG and Lei ZHANG. <br> Accepted by CVPR 2023.<br>

• News (2024-01-08): We provide the BaiduDrive Link towards the Training datset parts in HGGT.

Copyright, License and Agreement for the HGGT dataset Usage

1. Please note that this dataset is made available for non-commercial academic research purposes ONLY.
2. You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
3. You agree not to further copy, publish or distribute any portion of the HGGT dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.
4. The image contents are released upon request for research purposes ONLY.
5. Any violation of this protocol will be at his own risk. If any of the images include your information and you would like to remove them, please kindly inform us, and we will remove them from our dataset immediately.

Abstract

How to generate the ground-truth (GT) image is a critical issue for training realistic image super-resolution (Real- ISR) models. Existing methods mostly take a set of highresolution (HR) images as GTs and apply various degradations to simulate the low-resolution (LR) counterparts. Though great progress has been achieved, such an LR-HR pair generation scheme has several limitations. First, the perceptual quality of HR images may not be high enough, limiting the quality of Real-ISR model output. Second, existing schemes do not consider much human perception in GT generation, and the trained models tend to produce oversmoothed results or unpleasant artifacts. With the above considerations, we propose a human guided GT generation scheme. We first elaborately train multiple image enhancement models to improve the perceptual quality of HR images, and enable one LR image having multiple HR counterparts. Human subjects are then involved to annotate the high quality regions among the enhanced HR images as GTs, and label the regions with unpleasant artifacts as negative samples. A human guided GT image dataset with both positive and negative samples is then constructed, and a loss function is proposed to train the Real-ISR models. Experiments show that the Real-ISR models trained on our dataset can produce perceptually more realistic results with less artifacts.

Overall illustration of our dataset generation:

For more details, please refer to our paper.

Getting started

• Clone this repo.

git clone https://github.com/ChrisDud0257/HGGT
cd HGGT

• We recommend that you just skip to Step 4 if you don't have enough time or energy to go through the whole dataset generation procedures from Step 1 to Step 3. So you could directly utilize our proposed dataset to train your own models!
• If you are interested in our data generation steps, then you are welcome to follow our integral dataset generation progress.
• We will also provide our data annotation software as well as the annotation tutorial in the future. We hope our attempts and experiences will help more researchers to generate their human guided dataset and extand our ideas to their research fields so as to make great success in the GT manipulation.
• All of the datasets, models, results could be download through GoogleDrive.

Step 1: Image enhancement (Optional, you could just skip to step4 and train models with our proposed HGGT dataset)

Installation. (Python 3 + NVIDIA GPU + CUDA. Recommend to use Anaconda)

cd ImageEnhancement
conda create --name enhance python=3.9
pip install -r requirements.txt
python setup.py develop

We build up our project based on the widely-used BasicSR codeframe. For more installation details, please refer to the BasicSR framework.

• Prepare the training and testing dataset by following this instruction.
• Prepare the pre-trained models by following this instruction.

Training

• Firstly, check and modify the yml file ./ImageEnhancement/options/train/EnhancementModels/train_EnhanceRCAN_01.yml, this train options folder has four different enhancement models, you could just modify the parameters according to your own requirements.
• Secondly, uncomment the commands in ./ImageEnhancement/demo.sh. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./basicsr/train.py -opt ./options/train/EnhancementModels/train_EnhanceRCAN_01.yml --auto_resume

• And then,

cd ImageEnhancement
sh demo.sh

Inference

• Firstly, please follow this instruction to prepare the dataset for inference.
• (Optional) Secondly, please follow this instruction to prepare our well-trained models for inference. Or you could just utilize your own trained models.
• Uncomment the commands in ./ImageEnhancement/demo.sh and modify --input, --output and --model_path. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./inference/inference_rcannoup.py --input /home/chendu/data2_hdd10t/chendu/dataset/ForMultiGT/2/MultiGT1600 \
--output ./results/EnhancedGT/01 --model_path ./experiments/pretrained_models/enhancement/RCAN_01.pth --tile_size 1200 --suffix _01

• And then,

cd ImageEnhancement
sh demo.sh

• If your GPU memory is limited, please reduce --tile_size to 800 or even smaller.
• We provide the full size enhanced GT images which are prapared for the patch selection in Step 2. For directly usage, please follow this instruction.
• Note that, if you want to try our data annotation software, please make sure the four enhanced images generated by four different enhance models are named ended with "_01", "_02", "_03", "_04" and be put into the corresponding folder "01", "02", "03", "04". The original GT images should also be put into the "original" folder. For example, the folder structure should be the following:

-EnhancedGT/
    -original/
        -img1.png
    -01/
        -img1_01.png
    -02/
        -img1_02.png
    -03/
        -img1_03.png
    -04/
        -img1_04.png

• "Original" folder means it has all of the original GT images. If the enhanced and original images are not placed in this structure, then the annotation software will not work.

Step 2: Patch selection (Optional)

We complete Step 2 and Step 3 on Windows Operating System.

• Firstly, please put all of the enhanced GTs and original GTs into the folder structure as has been illustrated in Step 1.
• Secondly, complete the patch selection through our code. Please modify --img_path and --save_path.
• We select patches according to the quantity of details and textures, which is measured by the standard deviation (std) of the patch in image domain and the std of high-frequency component in a Laplacian pyramid, accordingly filter out the patches which have large smooth background regions.Thus, --cont_var_thresh and --freq_var_thresh control the final total saved amounts of the patches, when they become large, the patches with little edge information will be filtered out, and less patches would be saved.

cd PatchSelection
python patch_select.py --img_path 'Path to EnhancedGT' --save_path 'Path to your own patch selection save path'

• The save path folder structures will be as follows:

-PatchSelection/
    -original/
        -img1-x-y.png
        -img1-x1-y1.png
    -01/
        -img1-x-y_01.png
        -img1-x1-y1_01.png
    -02/
        -img1-x-y_02.png
        -img1-x1-y1_02.png
    -03/
        -img1-x-y_03.png
        -img1-x1-y1_03.png
    -04/
        -img1-x-y_04.png
        -img1-x1-y1_04.png

• Where x, y, x1, y1 record the top-left pixel position of the selected patches.
• Then you are recommended to filter out the patches manually for which the difference between the original version and the enhanced version is small (i.e., no much enhancement).
• In order to annotate the GTs more efficiently in Step 3, you are recommanded to disorder the GTs and re-group them into multiple groups. In our annotation progress, we distribute 1000 sets of images to every volunteers. In every set, the five GT images(i.e.original GT and the corresponding 01, 02, 03, 04 enhanced GTs) are randomly chosed without repetition from the original "PatchSelection" folder.

cd PatchSelection
python regroup.py --img_path 'Path to the patch selection save path' --save_path 'Path to your own regroup save path'

Step 3: Annotation (Optional)

We will provide the annotation software as well as the software tutorial in the future.

Ours HGGT dataset Structure

After the annotation step, we integrate the images and labels into the following folder structure:

Train/
    images/
        0-20193/
            original/
                img0-x-y.png
                img1-x1-y1.png
            01/
                img0-x-y_01.png
                img1-x1-y1_01.png
            02/
                img0-x-y_02.png
                img1-x1-y1_02.png
            03/
                img0-x-y_03.png
                img1-x1-y1_03.png
            04/
                img0-x-y_04.png
                img1-x1-y1_04.png
    labels/
        0-20193/
            A/
                img0-x-y.json
                img1-x1-y1.json
            B/
                img0-x-y.json
                img1-x1-y1.json
            C/
                img0-x-y.json
                img1-x1-y1.json

• For example, for each set of GTs, the original GT image img0-x-y.png has its corresponding enhanced versions, img0-x-y_01.png, img0-x-y_02.png, img0-x-y_03.png, img0-x-y_04.png, the five images are placed in the folder original/, 01/, 02/, 03/, 04/ respectively.

• As for the label, in the labels/0-20193/ folder, the three different folder A/, B/, C/ means each set of images is annotated by three differnt people, we denote A, B, C for representing the three people. For example, the image set img0-x-y.png, img0-x-y_01.png, img0-x-y_02.png, img0-x-y_03.png, img0-x-y_04.png are annotated by person 'A', and the annotation information is saved in the file labels/0-20193/A/img0-x-y.json. The img0-x-y.json has the same name with the original GT image img0-x-y.png, and it records the total four enhanced GTs' annotation results.

• In each annotation label, the .json file has the following data structures:

Picture_1 {'Name': '0001-586-1469.png', 'File': 'original', 'Label': None}
Picture_2 {'Name': '0001-586-1469_01.png', 'File': '01', 'Label': 'Positive'}
Picture_3 {'Name': '0001-586-1469_02.png', 'File': '02', 'Label': 'Positive'}
Picture_4 {'Name': '0001-586-1469_03.png', 'File': '03', 'Label': 'Positive'}
Picture_5 {'Name': '0001-586-1469_04.png', 'File': '04', 'Label': 'Positive'}
Time_cost {'Start': '2022-08-05 16:56:06', 'End': '2022-08-05 16:56:49', 'Total': 42}

• For more information, such as downloading and usage for train SR models in Step 4, please refer to the dataset instruction.

Step 4: Train realistic Super-resolution models on our proposed HGGT dataset

Installation. (Python 3 + NVIDIA GPU + CUDA. Recommend to use Anaconda)

cd PosNegGTSR
conda create --name posnegsr python=3.9
pip install -r requirements.txt
python setup.py develop

We build up our project based on the widely-used BasicSR codeframe. For more installation details, please refer to the BasicSR framework.

• Prepare the training and testing dataset by following this instruction.
• Prepare the pre-trained models by following this instruction.

Pre-train with out GAN (Optional)

• Following Real-ESRGAN, we pre-train a PSNR-model on DF2K_OST dataset with blind degradation factors and without a discriminator.
• This step is optional, you could just skip this step and ulitize our provided well-trained model to fine-tune your GAN-based models with our proposed PosNegGT dataset.
• Firstly, check and modify the yml file ./PosNegGTSR/options/train/PosNegGTSR/train_RRDB_DF2K_OST_Blind_x4.yml.
• Secondly, uncomment the commands in ./PosNegGTSR/demo.sh. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./basicsr/train.py -opt ./options/train/PosNegGTSR/train_RRDB_DF2K_OST_Blind_x4.yml --auto_resume

• And then,

cd PosNegGTSR
sh demo.sh

Training with a GAN with the original GT in our HGGT dataset or with DF2K_OST dataset.

• Original GT in HGGT dataset

• Firstly, check and modify the yml file ./PosNegGTSR/options/train/PosNegGTSR/train_RRDBGAN_PosNegGT_Blind_Ori_x4.yml. We fine-tune our GAN-model with a well-trained PSNR-model.

• Secondly, uncomment the commands in ./PosNegGTSR/demo.sh. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./basicsr/train.py -opt ./options/train/PosNegGTSR/train_RRDBGAN_PosNegGT_Blind_Ori_x4.yml --auto_resume

• And then,

cd PosNegGTSR
sh demo.sh

• DF2K_OST dataset

• Firstly, check and modify the yml file ./PosNegGTSR/options/train/PosNegGTSR/train_LDL_DF2K_OST_Blind_x4.yml. We fine-tune our GAN-model with a well-trained PSNR-model.

• Secondly, uncomment the commands in ./PosNegGTSR/demo.sh. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./basicsr/train.py -opt ./options/train/PosNegGTSR/train_LDL_DF2K_OST_Blind_x4.yml --auto_resume

• And then,

cd PosNegGTSR
sh demo.sh

Trainging with the positive GTs in our HGGT dataset.

• Firstly, check and modify the yml file ./PosNegGTSR/options/train/PosNegGTSR/train_RRDBGAN_PosNegGT_Blind_Pos_x4.yml. We fine-tune our GAN-model with a well-trained PSNR-model.
• Secondly, uncomment the commands in ./PosNegGTSR/demo.sh. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./basicsr/train.py -opt ./options/train/PosNegGTSR/train_RRDBGAN_PosNegGT_Blind_Pos_x4.yml --auto_resume

• And then,

cd PosNegGTSR
sh demo.sh

Trainging with both positive and negative GTs in our HGGT dataset.

• Firstly, check and modify the yml file ./PosNegGTSR/options/train/PosNegGTSR/train_RRDBGAN_PosNegGT_Blind_Pos+Neg_x4.yml. We fine-tune our GAN-model with a well-trained PSNR-model.
• Secondly, uncomment the commands in ./PosNegGTSR/demo.sh. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./basicsr/train.py -opt ./options/train/PosNegGTSR/train_RRDBGAN_PosNegGT_Blind_Pos+Neg_x4.yml --auto_resume

• And then,

cd PosNegGTSR
sh demo.sh

Step 5: Testing with the models trained in step 4

• Prepare the testing dataset by following this instruction.
• Prepare the pre-trained models by following this instruction.

To download our pre-trained models and for usage, please follow this instruction.

Testing with the model trained on the original GT in our proposed HGGT dataset or DF2K_OST dataset.

• Model trained with Original GT in HGGT dataset

• Firstly, check and modify the yml file ./PosNegGTSR/options/test/PosNegGTSR/test_RRDBGAN_PosNegGT_Blind_Ori_x4.yml.

• Secondly, uncomment the commands in ./PosNegGTSR/demo.sh. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./basicsr/test.py -opt ./options/test/PosNegGTSR/test_RRDBGAN_PosNegGT_Blind_Ori_x4.yml

• And then,

cd PosNegGTSR
sh demo.sh

• Model trained with DF2K_OST dataset

• Firstly, check and modify the yml file ./PosNegGTSR/options/test/PosNegGTSR/test_LDL_DF2K_OST_Blind_x4.yml.

• Secondly, uncomment the commands in ./PosNegGTSR/demo.sh. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./basicsr/test.py -opt ./options/test/PosNegGTSR/test_LDL_DF2K_OST_Blind_x4.yml

• And then,

cd PosNegGTSR
sh demo.sh

• Model trained with positive GT in our HGGT datset

• Firstly, check and modify the yml file ./PosNegGTSR/options/test/PosNegGTSR/test_RRDBGAN_PosNegGT_Blind_Pos_x4.yml.

• Secondly, uncomment the commands in ./PosNegGTSR/demo.sh. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./basicsr/test.py -opt ./options/test/PosNegGTSR/test_RRDBGAN_PosNegGT_Blind_Pos_x4.yml

• And then,

cd PosNegGTSR
sh demo.sh

• Model trained with both positive and negative GT in our HGGT datset

• Firstly, check and modify the yml file ./PosNegGTSR/options/test/PosNegGTSR/test_RRDBGAN_PosNegGT_Blind_Pos+Neg_x4.yml.

• Secondly, uncomment the commands in ./PosNegGTSR/demo.sh. For example:

CUDA_VISIBLE_DEVICES=0 \
python ./basicsr/test.py -opt ./options/test/PosNegGTSR/test_RRDBGAN_PosNegGT_Blind_Pos+Neg_x4.yml

• And then,

cd PosNegGTSR
sh demo.sh

Step 6: Evaluation metrics

• Prepare testing dataset by following this instruction.
• Our Test-100 testing dataset has 100 512*512 HR images together with their enhanced GTs. In each set of images, we place the original HR, the enhanced 01, 02, 03, 04 GTs. Every original GT has at least 2 positive enhanced GTs.
• For each SR result, we compute the metrics with the corresponding enhanced positive GT versions and then average all of the values which are computed with the different positive GTs.

PSNR & SSIM & LPIPS & DISTS

• Firstly, check and modify the .py file ./PosNegGTSR/scripts/metrics/calculate_multigt_labeled_psnr_ssim.py, ./PosNegGTSR/scripts/metrics/calculate_multigt_labeled_lpips.py, ./PosNegGTSR/scripts/metrics/calculate_multigt_labeled_dists.py
• Please modify --gts [Path to the Test-100 GT images file], --restored [path to your SR results], --json_path [Path to Test-100 label file].
• Secondly, uncomment the commands in ./PosNegGTSR/demo.sh. For example:

python ./scripts/metrics/calculate_multigt_labeled_psnr_ssim.py
python ./scripts/metrics/calculate_multigt_labeled_lpips.py
python ./scripts/metrics/calculate_multigt_labeled_dists.py

• And then,

cd PosNegGTSR
sh demo.sh

SR results

We also provide all of the visualization and quantitative metrics results reported in our paper, you could download through GoogleDrive.

License

This project is released under the Apache 2.0 license.

Citation

@InProceedings{Chen_2023_CVPR,
    author    = {Chen, Du and Liang, Jie and Zhang, Xindong and Liu, Ming and Zeng, Hui and Zhang, Lei},
    title     = {Human Guided Ground-Truth Generation for Realistic Image Super-Resolution},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {14082-14091}
}

Acknowledgement

This project is built mainly based on the excellent BasicSR and KAIR codeframe. We appreciate it a lot for their developers.

Contact

If you have any questions or suggestions about this project, please contact me at csdud.chen@connect.polyu.hk .