In this work, we propose a three-stage specular highlight removal network. To support network training and quantitative evaluation, we also present a large-scale synthetic dataset.
Towards High-Quality Specular Highlight Removal by Leveraging Large-scale Synthetic Data
Gang Fu, Qing Zhang, Lei Zhu, Chunxia Xiao, and Ping Li
In ICCV 23
The following figure presents the pipeline of our three-stage framework. It consists of three stages: (i) physics-based specular highlight removal; (ii) specular-free refinement; and (iii) tone correction. Specifically, in the first stage (see (a)), we decompose an input image into its albedo and shading using two encoder-decoder networks (
conda create --yes --name TSHRNet python=3.9
conda activate TSHRNet
conda install --yes pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 pytorch-cuda=11.6 -c pytorch -c nvidia
conda install --yes tqdm matplotlib
Please see "dependencies_install.sh".
- Our SSHR dataset is available at Google Drive or OneDrive (~5G).
- The SHIQ dataset can be found in the project SHIQ.
- The PSD dataset can be found in the project SpecularityNet-PSD.
The bash shell script file of "train.sh" provides the command lines for traning on different datasets.
python train_4_networks.py \
-trdd dataset \
-trdlf dataset/SSHR/train_7_tuples.lst \
-dn SSHR
python train_4_networks_mix.py \
-trdd dataset \
-trdlf dataset/SHIQ_data_10825/train.lst \
-dn SHIQ
python train_4_networks_mix.py \
-trdd dataset \
-trdlf dataset/M-PSD_Dataset/train.lst \
-dn PSD_debug_1
cat dataset/SSHR/train_4_tuples.lst dataset/SHIQ_data_10825/train.lst dataset/M-PSD_Dataset/train.lst >> dataset/train_mix.lst
python train_4_networks_mix.py \
-trdd dataset \
-trdlf dataset/train_mix.lst \
-dn mix_SSHR_SHIQ_PSD
The bash shell script file of "test.sh" provides the command lines for testing on different datasets.
Note thatwe split "test.lst" into four parts for testin, due to out of memory.
num_checkpoint=60 # the indexes of the used checkpoints
model_name='SSHR' # find the checkpoints in "checkpoints_${model_name}, like "checkpoints_SSHR"
testing_data_name='SSHR' # testing dataset name
# processing testing images
python test_4_networks.py -mn ${model_name} -l ${num_checkpoint} -tdn ${testing_data_name} -tedd 'dataset' -tedlf 'dataset/SSHR/test_7_tuples_part1.lst'
python test_4_networks.py -mn ${model_name} -l ${num_checkpoint} -tdn ${testing_data_name} -tedd 'dataset' -tedlf 'dataset/SSHR/test_7_tuples_part2.lst'
python test_4_networks.py -mn ${model_name} -l ${num_checkpoint} -tdn ${testing_data_name} -tedd 'dataset' -tedlf 'dataset/SSHR/test_7_tuples_part3.lst'
python test_4_networks.py -mn ${model_name} -l ${num_checkpoint} -tdn ${testing_data_name} -tedd 'dataset' -tedlf 'dataset/SSHR/test_7_tuples_part4.lst'
num_checkpoint=60
model_name='SHIQ'
testing_data_name='SHIQ'
python test_4_networks_mix.py -mn ${model_name} -l ${num_checkpoint} -tdn ${testing_data_name} -tedd 'dataset' -tedlf 'dataset/SHIQ_data_10825/test.lst'
num_checkpoint=60
model_name='PSD'
testing_data_name='PSD'
python test_4_networks_mix.py -mn ${model_name} -l ${num_checkpoint} -tdn ${testing_data_name} -tedd 'dataset' -tedlf 'dataset/M-PSD_Dataset/test.lst'
Please, put the SSHR, SHIQ, and PSD datasets into the directory of "dataset".
For seven-tuples image groups (i.e. including additional albedo and shading), their index structure has the following forms:
SSHR/train/048721/0024_i.jpg SSHR/train/048721/0024_a.jpg SSHR/train/048721/0024_s.jpg SSHR/train/048721/0024_r.jpg SSHR/train/048721/0024_d.jpg SSHR/train/048721/0024_d_tc.jpg SSHR/train/048721/0024_m.jpg
SSHR/train/048721/0078_i.jpg SSHR/train/048721/0078_a.jpg SSHR/train/048721/0078_s.jpg SSHR/train/048721/0078_r.jpg SSHR/train/048721/0078_d.jpg SSHR/train/048721/0078_d_tc.jpg SSHR/train/048721/0024_m.jpg
... ...
From left to right, they are input, albedo, shading, specular residue, diffuse, tone-corrected diffuse, and object mask images, respectively.
For four-tuples image groups, their index structure has the following forms (taking our SSHR as an example).
SSHR/train/048721/0044_i.jpg SSHR/train/048721/0044_r.jpg SSHR/train/048721/0044_d.jpg SSHR/train/048721/0044_d_tc.jpg
SSHR/train/048721/0023_i.jpg SSHR/train/048721/0023_r.jpg SSHR/train/048721/0023_d.jpg SSHR/train/048721/0023_d_tc.jpg
... ...
From left to right, they are input, specular residue, diffuse, and tone-corrected diffuse images, respectively. The main reason for is that it allows to be trained with four-tuples image grops of the SHIQ and PSD datasets. Please download our SSHR dataset and see it for more details.
For SHIQ, four-tuples image groups are like:
SHIQ_data_10825/train/00583_A.png SHIQ_data_10825/train/00583_S.png SHIQ_data_10825/train/00583_D.png SHIQ_data_10825/train/00583_D_tc.png
SHIQ_data_10825/train/08766_A.png SHIQ_data_10825/train/08766_S.png SHIQ_data_10825/train/08766_D.png SHIQ_data_10825/train/08766_D_tc.png
... ...
For PSD, their images can be constructed as the above form in a list file.
@inproceedings{zhang-2017-stack,
author = {Fu, Gang and Zhang, Qing and Zhu, Lei and Xiao, Chunxia and Li, Ping},
title = {Towards high-quality specular highlight removal by leveraging large-scale synthetic data},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
year = {2023},
pages = {To appear},
}
If you have any questions about this project, please contact me by [email protected]