Search Results for author: Abdelrahman Abdelhamed

Found 9 papers, 4 papers with code

Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images

no code implementations ICCV 2023 Donghwan Seo, Abhijith Punnappurath, Luxi Zhao, Abdelrahman Abdelhamed, Sai Kiran Tedla, Sanguk Park , Jihwan Choe, Michael S. Brown

The narrowing domain gap between real and synthetic imagery makes it possible to use CG images as training data for deep learning models targeting high-level computer vision tasks, such as autonomous driving and semantic segmentation.

Autonomous Driving Denoising +2

Day-to-Night Image Synthesis for Training Nighttime Neural ISPs

1 code implementation CVPR 2022 Abhijith Punnappurath, Abdullah Abuolaim, Abdelrahman Abdelhamed, Alex Levinshtein, Michael S. Brown

Training nightmode ISP networks relies on large-scale datasets of image pairs with: (1) a noisy raw image captured with a short exposure and a high ISO gain; and (2) a ground truth low-noise raw image captured with a long exposure and low ISO that has been rendered through the ISP.

Image Generation

Leveraging the Availability of Two Cameras for Illuminant Estimation

no code implementations CVPR 2021 Abdelrahman Abdelhamed, Abhijith Punnappurath, Michael S. Brown

In this paper, we leverage the availability of these two cameras for the task of illumination estimation using a small neural network to perform the illumination prediction.

Vocal Bursts Valence Prediction

A High-Quality Denoising Dataset for Smartphone Cameras

no code implementations CVPR 2018 Abdelrahman Abdelhamed, Stephen Lin, Michael S. Brown

We propose a systematic procedure for estimating ground truth for noisy images that can be used to benchmark denoising performance for smartphone cameras.

Image Denoising Vocal Bursts Intensity Prediction

Two Illuminant Estimation and User Correction Preference

no code implementations CVPR 2016 Dongliang Cheng, Abdelrahman Abdelhamed, Brian Price, Scott Cohen, Michael S. Brown

Existing methods attempt to estimate a spatially varying illumination map, however, results are error prone and the resulting illumination maps are too low-resolution to be used for proper spatially varying white-balance correction.

Vocal Bursts Valence Prediction

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