Mahmoud Afifi
PhD in Computer Science, York University, Canada | Supervisor: Prof. Michael S. Brown
MSc in Information Technology, Assiut University, Egypt | Supervisor: Prof. Khaled Hussain
Email: m.3[last name][at]gmail[dot]com
CV | Linkedin | Google Scholar | ResearchGate | GitHub | Mathworks | Twitter | Instagram
Experience
Machine Learning / Camera Algorithms Engineer: Worked on color correction of iPhone cameras.
Consultant: Worked on image harmonization.
Student Researcher / Research Intern: Worked with Gcam team on auto white-balance correction.
Post-doc Visitor: Worked on raw image mapping, image stylization, and white-balance correction.
Research Intern: Contributed to My Filters feature (released in Samsung Galaxy S20) and developed techniques for color and exposure-error correction.
Computer Vision R&D Engineer / Consultant: Developed an ML algorithm for skin color correction and consulted on hairstyle editing and hair color matching used in LUXY HAIR virtual demo.
Research Engineer / Consultant: Developed the color correction module in NUDEMETER and consulted on skin tone analysis.
Research
I am interested in low-level computer vision and computational photography. Much of my research is about color processing and enhancing photograph quality. Representative examples of my work are shown below. For my full list of publications, please check here.
Patents
Mahmoud Afifi, Michael S. Brown, Brian Price, and Scott Cohen. Image White Balancing. US Patent, 2021 | Adobe Inc & York Univ
Atima Lui, Nyalia Lui, Mahmoud Afifi, and Ariadne Bazigos. System and Method for Color Matching. US Patent, 2020 | My Nudest Inc
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Papers
Mahmoud Afifi, Marcus A. Brubaker, and Michael S. Brown
WACV 2022
35% acceptance rateMixed/single-illuminant scene white balancing does not necessarily require illuminant estimation. Instead, the problem could be bounded by a small set of predefined white-balance settings. Given that, we locally blend a set of small images rendered with different white-balance settings to generate the final corrected image.
Paper | Supp. Materials | arXiv | Code & Data | Poster | Presentation | Talk | Patent Application
Mahmoud Afifi, Jonathan T. Barron, Chloe LeGendre, Yun-Ta Tsai, and Francois Bleibel
ICCV 2021 (Oral Presentation)
25.9% acceptance rate | 3% oral presentation acceptance rateGoogle Research
A self-calibration method for cross-camera color constancy through the lens of transductive inference: additional (unlabeled) images are provided as input to the model at test time, which allows the model to calibrate itself to the spectral properties of the test-set camera during inference.
Paper | Supp. Materials | arXiv | Code | Poster | Presentation | Presentation (PDF) | Talk | Google @ ICCV'21
Mahmoud Afifi, Marcus A. Brubaker, and Michael S. Brown
CVPR 2021
23.4% acceptance rateHistoGAN is the first work to control colors of GAN-generated images based on features derived directly from color histograms. Our method learns to transfer the color information encapsulated in histogram features to the colors of a GAN-generated images (HistoGAN) or real input images (ReHistoGAN). As color histograms provide an abstract representation of image color that is decoupled from spatial information, our HistoGAN and ReHistoGAN are less restrictive and suitable across arbitrary domains.
Paper | Supp. Materials | arXiv | Code & Dataset | Colab (histogram loss) | Poster | Presentation | Talk
Mahmoud Afifi, Konstantinos G. Derpanis, Björn Ommer, and Michael S. Brown
CVPR 2021
23.4% acceptance rateSamsung Research
A single coarse-to-fine deep learning model with adversarial training to correct both over- and under-exposed photographs.
Paper | Supp. Materials | arXiv | Code & Dataset | Poster | Presentation | Talk | Samsung Research Post | Patent Application
Mahmoud Afifi, Abdelrahman Abdelhamed, Abdullah Abuolaim, Abhijith Punnappurath, and Michael S. Brown
TPAMI 2021
Impact factor: 16.389Learning an accurate camera-rendering linearization gives a significant improvement for different computer vision tasks (e.g., denoising, deblurring, and image enhancement).
Paper | arXiv | Supp. Materials | External Link | Code & Dataset
Mahmoud Afifi and Abdullah Abuolaim
BMVC 2021
A semi-supervised method to map between two different camera-raw spaces. Training requires an unpaired set of images besides a very small set of paired images taken by these two camera models.
arXiv | Dataset | Presentation
Mahmoud Afifi and Michael S. Brown
CVPR 2020 (Oral Presentation)
22.1% acceptance rate | 5.7% oral presentation acceptance rateSamsung Research
A multi-task deep learning model for post-capture white-balance correction and editing
Paper | Supp. Materials | arXiv | Code | Presentation | Talk | CVPR Daily Magazine | Samsung Research Post | Samsung Newsroom | Patent Application
Abhijith Punnappurath, Abdullah Abuolaim*, Mahmoud Afifi*, and Michael S. Brown
ICCP 2020
A symmetry property of dual-pixel kernels for unsupervised depth estimation
* Equal contribution
Paper | Code & Dataset | Talk
Majed El Helou, Ruofan Zhou, Sabine Süsstrunk, Radu Timofte, Mahmoud Afifi, et al.,
ECCV workshops 2020
Runner-Up Award overall tracks
As an intermediate stage, producing a uniformly-lit white-balanced image could help to eventually produce high-quality relit images.
arXiv | Code | Certificate
Mahmoud Afifi and Michael S. Brown
CIC 2020 (Oral Presentation)
A simple method to link the nonlinear white-balance correction functions, introduced in our CVPR'19 work, to the user's selected colors to allow interactive white-balance manipulation
arXiv | Code | Presentation
Hoang Le, Mahmoud Afifi, and Michael S. Brown
CIC 2020 (Oral Presentation)
Having additional wide-gamut metadata, available during color space conversion, greatly assists in constructing a locally weighted color mapping function to convert between color gamuts.
Mahmoud Afifi and Michael S. Brown
ICCV 2019
25% acceptance rateDeep learning models can be fooled by white-balance errors and their accuracy can be improved by augmented images with different white-balance settings.
Paper | Supp. Materials | arXiv | Project Page | Code | Colab
Mahmoud Afifi, Brian Price, Scott Cohen, and Michael S. Brown
CVPR 2019
25.2% acceptance rateIn collaboration with Adobe Research
The first work to directly address the problem of incorrectly white-balanced images; requires a small memory overhead and it is fast.
Paper | Project Page | Supp. Materials | Demo | Video | Code | Dataset | Adobe Research Post | Patent
Mahmoud Afifi, Brian Price, Scott Cohen, and Michael S. Brown.
Eurographics 2019 (Short Papers)
In collaboration with Adobe Research
A fully automated image recoloring without needing for target/reference images
Paper | Project Page | Code | Supp. Materials | Presentation | Fast-Forward Video | Adobe Research Post
Mahmoud Afifi and Michael S. Brown
BMVC 2019 (Oral Presentation)
28% acceptance rate | 5% oral presentation acceptance rateLearning a new canonical space in an unsupervised manner allows us to train a single deep model on multiple camera sensors and perform accurate illuminant estimation for images captured by new unseen camera sensors in the inference phase.
Paper | Project Page | Supp. Materials | arxiv | Code | Presentation | Talk | Patent Application
Mahmoud Afifi, Abhijith Punnappurath, Abdelrahman Abdelhamed, Hakki Can Karaimer, Abdullah Abuolaim, and Michael S. Brown
CIC 2019 (Oral Presentation)
Best paper award
With a small modification on existing camera ISPs, we can easily get accurate post-capture white-balance editing.
Paper | Project Page | Code | Presentation | Patent Application
Mahmoud Afifi, Abhijith Punnappurath, Graham Finlayson, and Michael S. Brown
JOSA A 2019
A locally adaptive bias correction technique for illuminant estimation
Paper | Project Page | Code
Mahmoud Afifi and Abdelrahman Abdelhamed
JVCI 2019
Gender classification can be improved using different facial features; our finding is validated by a user study.
Mahmoud Afifi
MTA 2019
Hand images can efficacy be used for gender recognition and biometric identification; a large dataset of hand images enables us to train our two-stream deep model.
Khaled F. Hussain, Mahmoud Afifi, Ghada Moussa
ITS 2018
Impact factor: 6.492In generic vehicle classification tasks, deep learning models tend to not rely on color information and filter out unnecessary details information in high-resolution images.
Abdullah Sawas*, Abdullah Abuolaim*, Mahmoud Afifi, and Manos Papagelis
MDM 2018
Best paper award
Efficient discovery of evolving groups of pedestrians; a new group pattern is introduced in the journal version.
* Equal contribution
Mahmoud Afifi and Khaled F. Hussain
CVM 2016
Bleeding artifacts caused by Poisson image editing can be reduced by a simple two-stage blending approach.
Paper | Project Page | Code | Video
Conference version: Paper | Project Page | Code | Video
Mahmoud Afifi, Khaled F. Hussain, Hosny M. Ibrahim, and Nagwa M. Omar
ISPACS 2015
In many scenarios, image registration and blending can help to get a fast video completion.
Awards & Honors
Dissertation Award
CS-Can|Info-Can Canadian Computer Science Distinguished Dissertation Award, 2021 | Lassonde post
CIPPRS John Barron Doctoral Dissertation Award, 2021 | Lassonde post
Nominated by EECS for the Best Doctoral Dissertation Prize at York University, 2021
Best Paper Award
Outstanding Reviewer
Challenges
Runner-Up Award overall tracks in AIM 2020 Challenge on Scene Relighting and Illumination Estimation at ECCV'20
Best Short CG Film in the Fourth Forum of Egyptian Faculties of Computer and Information Science, 2010
Other Awards
Professional Services
Program Committee Member:
Reviewer:
Conferences: SIGGRAPH Asia'22, CVPR'22, WACV'22, SIGGRAPH Asia'21, SIGGRAPH '21, ICCV'21, CVPR'21, BMVC'21, WACV'21, MASCOTS'21, CVPR'20, WACV'20, ACCV'20, MASCOTS'20, CIC28, CRV'20, WACV'19, CIC27, BMVC'19, BMVC'18
Journals: T-PAMI, T-IP, IJCV, T-CI, T-MM, VTM, T-ITS, T-CSVT, T-HMS, T-MECH, T-CE, IEEE Access, TOMM, TVCJ, MTAP, JVCI, MULT, MVAP, SIVP, JRTIP, IET IP, IET CV, IET SP, IET EL, SPIE JEI, CIN, JHE, OPENCS
Vice-Chair: ACM Assiut Student Chapter