Staff Research Scientist at AI Center-Toronto, Samsung Electronics
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
Staff Research Scientist / Research Intern: Works on low-level CV, camera ISP, and imaging technologies. As an intern, I contributed to My Filters feature (released in Samsung Galaxy S20) and developed techniques for color and exposure-error correction.
Camera Software Engineer / Student Researcher / Research Intern: Worked with Pixel team on color correction of Pixel phone cameras. As a student researcher/intern with the Gcam team, I worked on cross camera white-balance correction (C5).
Machine Learning / Camera Algorithms Engineer: Worked with Camera ISP Algorithm on color correction for iPhone cameras.
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.
Consultant: Worked on image harmonization.
Research Engineer / Consultant: Developed the color correction module in NUDEMETER and consulted on skin tone analysis.
I am interested in low-level computer vision and computational photography, focusing on color processing and enhancing photograph quality. Below are selected examples of my work. For a complete list of publications, please click here.
Mahmoud Afifi*, Luxi Zhao*, Abhijith Punnappurath, Mohamed A. Abdelsalam, Ran Zhang, and Michael S. Brown
arXiv, 2025
AI Center-Toronto, Samsung Electronics
Timestamp and geolocation, combined with capture metadata, provide strong cues for estimating scene illuminants in smartphone camera white balancing. Our method leverages this data, along with color information, using a lightweight learnable model (~5K parameters) that runs efficiently on a flagship mobile DSP (0.25 ms) and CPU (0.80 ms), achieving high accuracy. We also introduce a large dataset (~3.2K raw images) from the S24 Ultra, containing ground-truth illuminants (neutral and user-preference-based) and capture metadata.
* Equal contribution
arXiv | Code & Data
Dongyoung Kim, Mahmoud Afifi, Dongyun Kim, Michael S. Brown, and Seon Joo Kim
arXiv, 2025
AI Center-Toronto, Samsung Electronics in collaboration with Yonsei University
By leveraging pre-calibrated color correction matrices (CCMs) existing in camera ISPs, we generate a compact camera fingerprint embedding to adapt our method to new cameras. Our method achieves state-of-the-art performance in color constancy across diverse cameras, while remaining lightweight.
arXiv | Project Page | Code
SaiKiran Tedla*, Junyong Lee*, Beixuan Yang, Mahmoud Afifi, and Michael S. Brown
arXiv, 2025
AI Center-Toronto, Samsung Electronics in collaboration with York University
We present a method for multispectral (MS) image demosaicing designed for dual-camera setups, leveraging co-captured high-fidelity RGB images to guide the reconstruction of lower-fidelity MS images. Alongside, we provide a large dataset of paired RGB and MS mosaiced images with ground-truth demosaiced outputs.
* Equal contribution
arXiv | Code & Data
Artem Nikonorov*, Georgy Perevozchikov*, Andrei Korepanov, Nancy Mehta, Mahmoud Afifi, Egor Ershov, and Radu Timofte
arXiv, 2025
In collaboration with the University of Würzburg, Samara University, and IITP RAS
We introduce cmKAN, a powerful color-matching framework that employs a hypernetwork to generate spatially adaptive weights for controlling KAN’s nonlinear splines. cmKAN achieves state-of-the-art color matching between diverse source and target distributions in supervised and unsupervised settings.
* Equal contribution
Abdelrahman Abdelhamed*, Mahmoud Afifi*, and Alec Go
arXiv, 2024
With some prompt engineering, multimodal LLMs (e.g., Gemini) can perform zero-shot image classification. However, they may not consistently produce accurate target dataset labels. Our approach leverages multimodal LLMs & cross-modal embedding encoders to produce initial class prediction feature & image description feature alongside image feature, improving zero-shot image classification accuracy without the need for dataset-specific prompts. Our method outperforms prior methods across various datasets, achieving a 6.8% increase in accuracy on ImageNet.
* Equal contribution
Mahmoud Afifi, Zhenhua Hu, and Liang Liang
ECCV 2024
27.9% acceptance rateUtilizing the chromatic distortion present between long and short exposure frames of HDR photography, we introduce a compact guiding feature for illuminant estimators. Processed by just ~300 learnable parameters, our method achieves results that match or surpass previous methods relying on thousands or even millions of parameters.
Paper | Supp. Materials | arXiv | Poster
Georgy Perevozchikov, Nancy Mehta, Mahmoud Afifi, and Radu Timofte
ECCV 2024
27.9% acceptance rateIn collaboration with the University of Würzburg
Rawformer, an unsupervised Transformer-based encoder-decoder model for raw-to-raw mapping, enables the utilization of learnable camera ISP trained on a specific camera's raw images to render raw images taken by new cameras with different characteristics.
Paper | Supp. Materials | arXiv | Code
Mahmoud Afifi, Marcus A. Brubaker, and Michael S. Brown
WACV 2022
35% acceptance rateYork University
Mixed/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
Abdullah Abuolaim, Mahmoud Afifi, and Michael S. Brown
WACV 2022
35% acceptance rateYork University
Jointly learning to predict the two DP views from a single blurry input image improves the network’s ability to learn to deblur the image. Generating high-quality DP views can be used for other DP-based applications, such as reflection removal.
PDF | Supplementary Materials | arXiv | Code & Dataset | 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 rateYork University
HistoGAN 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 rateSAIC, Samsung Research America in collaboration with Heidelberg University
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
Mahmoud Afifi, Abdelrahman Abdelhamed, Abdullah Abuolaim, Abhijith Punnappurath, and Michael S. Brown
TPAMI 2021
Impact factor: 20.8 (2023)York University
Learning 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
York University
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 rateSAIC, Samsung Research America
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
Abhijith Punnappurath, Abdullah Abuolaim*, Mahmoud Afifi*, and Michael S. Brown
ICCP 2020
York University
A symmetry property of dual-pixel kernels for unsupervised depth estimation
* Equal contribution
Paper | Code & Dataset | Talk
Atima Lui, Nyalia Lui, Mahmoud Afifi, and Ariadne Bazigos
US Patent 2020
My Nudest Inc
A system for analyzing user input, combining user's image(s) and query responses to provide tailored color outputs. Through color correction and comparison to predetermined color identifiers, it delivers accurate results and product recommendations.
Mahmoud Afifi and Michael S. Brown
CIC 2020 (Oral Presentation)
York University
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)
York University
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 rateYork University
Deep 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, Abhijith Punnappurath, Abdelrahman Abdelhamed, Hakki Can Karaimer, Abdullah Abuolaim, and Michael S. Brown
CIC 2019 (Oral Presentation)
Best paper award
York University
With a small modification to existing camera ISPs, we can achieve accurate post-capture white balance editing by embedding a set of mapping coefficients in the JPEG metadata.
Paper | Project Page | Code | Presentation | Patent Application
Mahmoud Afifi, Brian Price, Scott Cohen, and Michael S. Brown.
Eurographics 2019 (Short Papers)
York University in collaboration with Adobe Research
A fully automated image recoloring without the need for target/reference images
Paper | Project Page | External Link | Code | Supp. Materials | Presentation | Fast-Forward Video | Adobe Research Post
Mahmoud Afifi, Brian Price, Scott Cohen, and Michael S. Brown
CVPR 2019
25.2% acceptance rateYork University in 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 and Michael S. Brown
BMVC 2019 (Oral Presentation)
28% acceptance rate | 5% oral presentation acceptance rateYork University
Learning 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
Abdullah Sawas*, Abdullah Abuolaim*, Mahmoud Afifi, and Manos Papagelis
MDM 2018
Best paper award
York University
Efficient discovery of evolving groups of pedestrians; a new group pattern is introduced in the journal version.
* Equal contribution
Mahmoud Afifi and Abdelrahman Abdelhamed
JVCI 2019
Impact factor: 2.6 (2023)Assiut University
Gender classification can be improved using different facial features; a user study validates our finding.
Mahmoud Afifi
MTA 2019
Impact factor: 3.0 (2023)Assiut University
Hand images can be used for gender recognition and biometric identification; a large dataset of hand images enables us to train our two-stream deep model.
Mahmoud Afifi and Khaled F. Hussain
CVM 2016
Impact factor: 17.3 (2023)Assiut University
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
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
European Conference on Computer Vision, 2024 (ECCV'24)
IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024 (CVPR'24)
IEEE/CVF International Conference on Computer Vision, 2021 (ICCV'21)
IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020 (CVPR'20)
Honourable Mention at British Machine Vision Conference, 2019 (BMVC'19)
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
Others
Google Advocate for the African Computer Vision Summer School (ACVSS) in Kenya, 2024
Program Committee Member:
AIM: Advances in Image Manipulation workshop in conjunction with ECCV 2024
NTIRE: New Trends in Image Restoration and Enhancement workshop and challenges in conjunction with CVPR 2024
NTIRE: New Trends in Image Restoration and Enhancement workshop and challenges in conjunction with CVPR 2023
NTIRE: New Trends in Image Restoration and Enhancement workshop and challenges in conjunction with CVPR 2022 | Apple post
NTIRE: New Trends in Image Restoration and Enhancement workshop and challenges in conjunction with CVPR 2020
Student Representative at Tenure & Promotion Adjudicating Committee, EECS, York University, 2020
Reviewer:
Conferences: ICCV'25, CVPR'25, ECCV'24, CVPR'24, ACCV'24, LIM'24, SIGGRAPH Asia'23, ICCV'23, CVPR'23, 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, CVM, VTM, T-ITS, T-CSVT, T-HMS, T-MECH, T-CE, Neurocomputing, IEEE Access, TOMM, TVCJ, MTAP, COL, Displays, JVCI, MULT, MVAP, SIVP, JRTIP, IET IP, IET CV, IET SP, IET EL, SPIE JEI, CIN, JHE, OPENCS
Vice-Chair: ACM Assiut Student Chapter
