Stacked_DMSHN_bokeh
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Official Repository for our CVPRW (MAI'21) paper.
Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image
Saikat Dutta, Sourya Dipta Das, Nisarg A. Shah and Anil Kumar Tiwari
Accepted at Mobile AI workshop, co-located with CVPR 2021 Paper | ArXiv | Supplementary | YouTube
Pytorch 1.1.0 Torchvision 0.3.0 skimage 0.16.2
![](https://github.com/saikatdutta/Stacked_DMSHN_bokeh/raw/main/assets/demo.gif)
![](https://github.com/saikatdutta/Stacked_DMSHN_bokeh/raw/main/assets/comparison.jpg)
1. Dataset:
Get the EBB! dataset by registering here.
Train split: data/train.csv
Test split (val294 set): data/test.csv
2. Run inference on Val294 set using DMSHN model:
python DMSHN_test.py
3. Run inference on Val294 set using Stacked DMSHN model:
python stacked_DMSHN_test.py
4. To generate PSNR, SSIM and LPIPS scores on output images:
python eval.py -d0 OUT_DIR -d1 GT_DIR --use_gpu
5. Citation:
@inproceedings{dutta2021stacked,
title={Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image},
author={Dutta, Saikat and Das, Sourya Dipta and Shah, Nisarg A and Tiwari, Anil Kumar},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2398--2407},
year={2021}
}
6. Related work:
[1] Dutta, Saikat. "Depth-aware blending of smoothed images for bokeh effect generation." Journal of Visual Communication and Image Representation (2021): 103089. Paper ArXiv Project page
[2] Das, Sourya Dipta, and Saikat Dutta. "Fast deep multi-patch hierarchical network for nonhomogeneous image dehazing." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020. Paper ArXiv Code
7. Useful Repositories:
[1] SSIM loss
[2] MSSSIM loss
[3] LPIPS