Abdelrahman Abdelhamed

Orcid: 0000-0002-4836-794X

According to our database1, Abdelrahman Abdelhamed authored at least 17 papers between 2017 and 2024.

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Bibliography

2024
Advocating Pixel-Level Authentication of Camera-Captured Images.
IEEE Access, 2024

2023
Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Learning Tone Curves for Local Image Enhancement.
IEEE Access, 2022

Extracting Vignetting and Grain Filter Effects from Photos.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Day-to-Night Image Synthesis for Training Nighttime Neural ISPs.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
GamutNet: Restoring Wide-Gamut Colors for Camera-Captured Images.
Proceedings of the 29th Color and Imaging Conference, 2021

Leveraging the Availability of Two Cameras for Illuminant Estimation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results.
CoRR, 2020


2019
AFIF<sup>4</sup>: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces.
J. Vis. Commun. Image Represent., 2019

Color Temperature Tuning: Allowing Accurate Post-Capture White-Balance Editing.
Proceedings of the 27th Color and Imaging Conference, 2019

Noise Flow: Noise Modeling With Conditional Normalizing Flows.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

MarkWhite: An Improved Interactive White-Balance Method for Smartphone Cameras.
Proceedings of the 45th Graphics Interface Conference 2019, 2019


2018
A High-Quality Denoising Dataset for Smartphone Cameras.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017
AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces.
CoRR, 2017


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