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Found 9 papers, 4 papers with code
Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images
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.
Day-to-Night Image Synthesis for Training Nighttime Neural ISPs
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.
Leveraging the Availability of Two Cameras for Illuminant Estimation
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.
CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks
Because of this, a number of methods have been proposed to "unprocess" nonlinear images back to a raw-RGB state.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
Noise Flow: Noise Modeling with Conditional Normalizing Flows
Modeling and synthesizing image noise is an important aspect in many computer vision applications.
Ranked #8 on
Image Denoising
on SID x300
A High-Quality Denoising Dataset for Smartphone Cameras
We propose a systematic procedure for estimating ground truth for noisy images that can be used to benchmark denoising performance for smartphone cameras.
AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces
Gender classification aims at recognizing a person's gender.
Two Illuminant Estimation and User Correction Preference
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.
