Mahmoud Afifi1 and Michael S. Brown1,2
1York University 2Samsung AI Center (SAIC) - Toronto
Project page
🔥 PyTorch implementation is now available. You can use this code to train the model on new datasets captured by various cameras/sensors and use the trained model with unseen cameras/sensors.
For Matlab version, it is strongly recommended to first try out the models for Matlab 2018b, if a compatibility error is faced, then try out the 'Matlab 2019b or higher' option.
For PyTorch implementation, please check this Colab notebook.
- Matlab 2018b or higher (recommended)
- Deep Learning Toolbox for Matlab 2018b or higher
The original experiments were done using Matlab 2018b. In some higher Matlab versions, models designed in Matlab 2018b do not work. For this reason, we provide another version of models that should work but may give higher errors than the Matlab 2018b models.
Run install_.m, then run demo.m to test our trained models. In demo.m, you should select the version of Matlab by changing the value of Matlab_ver. The supported versions are: Matlab 2018b, Matlab 2019a, or higher. It is highly recommended to set Matlab_ver to Matlab 2018b (even if you use higher version). If you got compatability error, then try out with Matlab_ver = 'Matlab 2019b'.
You can change the model_name and image_name variables to choose between our trained models and to change input image filename, respectively. You can test any of our trained models located in models directory. Each model was trained using different camera sensors, as discussed in our paper. Each model is named based on the validation set used during the training (for example, the model trained_model_wo_CUBE+_CanonEOS550D.mat was trained using all raw-RGB linear images from NUS and Gehler-Shi datasets without including any example from the CanonEOS550D camera in Cube/Cube+ datasets).
The input image file must contain the image raw-RGB values after applying the black/saturation level normalization. This is very important since all trained networks expect to get uint16 input images after applying the black/saturation level normalization.
No. Our method works with linear raw-RGB images, not camera-rendered images. To corret your sRGB-rendered images, you can check When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images, CVPR'19 for white balancing sRGB-rendered images (an online demo is provided).
Can I test images captured by camera sensors different than the camera sensors used for training (i.e., NUS, Gehler-Shi, and Cube/Cube+ datasets)?
Yes. Our work is proposed to reduce the differences between camera sensor responses (that is why mostly all of learning-based illuminant estimation models are sensor-dependent and cannot generalize well for new camera sensors -- see On Finding Gray Pixels, CVPR'19 for interesting experiments that highlight this point). Our method, however, targets to work independently from the camera sensor. Read our paper for more details.
First, be sure that all testing images are in the raw-RGB linear space and the black level/saturation normalization is correctly applied. The input images should be stored as uint16 PNG files after the normalization. For Matlab, you can use any of our trained models for testing. You can report results of one model or the best, mean, and worst results obtained by all models. A Matlab example code to evaluate testing image set is provided in evaluate_images.m. You can also use all trained models and report the averaged illuminant vectors for evaluation (i.e., ensemble model). Alternatively, you can train on your own data and test on images captured by unseen camera using our PyTorch implementation.
In the given demo, we show raw-RGB images after white balancing and scaling it up by a constant factor to aid visualization. In the paper, we used the full camera pipeline in A Software Platform for Manipulating the Camera Imaging Pipeline, ECCV'16 to render images to the sRGB space with our estimated illuminant vector.
For PyTorch, you can create an instance of our Histogram block and integrate it to your network. For example self._hist_block = RGBuvHistBlock(insz=img_size, h=hist_bin).
For Matlab 2018b and 2019a, please check examples given in RGBuvHistBlock/add_RGB_uv_hist.m. If you will use the RGB-uv histogram block for sRGB-rendered images (e.g., JPEG images), you may need to tune the initalization of the scale and fall-off parameters for better results with sRGB images, as the current intalization was used for linear raw-RGB images. To tune these parameters, you can change the initalization of the scale parameter C in scaleLayer.m (line 39) and the fall-off factor sigma in ExponentialKernelLayer.m (line 43). The files scaleLayer.m and ExponentialKernelLayer.m are located in RGBuvHistBlock directory. For debugging, please use the predict function in histOutLayer.m. For Matlab 2019b or higher (recommended), please check the RGBuvHistBlock.m code located in the RGBuvHistBlock directory to tune the scale/fall-off parameters.
If you use this code, please cite our paper:
Mahmoud Afifi and Michael S. Brown, Sensor Independent Illumination Estimation for DNN Models, British Machine Vision Conference (BMVC), 2019.
@inproceedings{afifi2019SIIE,
title={Sensor-Independent Illumination Estimation for DNN Models},
author={Afifi, Mahmoud and Brown, Michael S},
booktitle={British Machine Vision Conference (BMVC)},
pages={},
year={2019}
}
This software is provided for research purposes only. A license must be obtained for any commercial application.
