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DoubleDIP

Official implementation of the paper "Double-DIP: Unsupervised Image Decomposition via Coupled Deep-Image-Priors"

Install / Use

/learn @yossigandelsman/DoubleDIP
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0/100

Supported Platforms

Universal

README

Double Dip

Official implementation of the paper "Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors.

Paper: http://www.wisdom.weizmann.ac.il/~vision/DoubleDIP/resources/DoubleDIP.pdf

Project page: http://www.wisdom.weizmann.ac.il/~vision/DoubleDIP/


sketch

If you find our work useful in your research or publication, please cite it:

@article{DoubleDIP,
author = {Gandelsman, Yossi and Shocher, Assaf and Irani, Michal},
year = {2019},
month = {6},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title = {"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors}
}

Further comments:

The airlight estimation in the dehazing part of the code uses the code provided by "Blind Dehazing Using Internal Patch Recurrence".

The saliency detection that is used for segmentation hints provided by Context-Aware Saliency Detection, by Gofman et al. After applying this saliency detection, we thresholded it using bg_fg_prep.py.

The code is provided as-is for academic use only and without any guarantees. Please contact the author to report any bugs.

Related Skills

View on GitHub
GitHub Stars523
CategoryDevelopment
Updated21d ago
Forks96

Languages

Python

Security Score

80/100

Audited on Mar 19, 2026

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