Noise Estimation
52 papers with code • 1 benchmarks • 1 datasets
Libraries
Use these libraries to find Noise Estimation models and implementations
SIDD
Most implemented papers
Unprocessing Images for Learned Raw Denoising
google-research/google-research
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CVPR 2019
Machine learning techniques work best when the data used for training resembles the data used for evaluation.
Pyramid Real Image Denoising Network
Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features.
Toward Convolutional Blind Denoising of Real Photographs
While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs.
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise.
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
To highlight, RP with a CNN classifier can predict if an MNIST digit is a "one"or "not" with only 0. 25% error, and 0. 46 error across all digits, even when 50% of positive examples are mislabeled and 50% of observed positive labels are mislabeled negative examples.
Automatic, fast and robust characterization of noise distributions for diffusion MRI
Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process.
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling
In this paper, we propose a grouped residual dense network (GRDN), which is an extended and generalized architecture of the state-of-the-art residual dense network (RDN).
Variational Denoising Network: Toward Blind Noise Modeling and Removal
zsyOAOA/VDNet
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NeurIPS 2019
On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression.
Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation
Specifically, we approximate the joint distribution with two different factorized forms, which can be formulated as a denoiser mapping the noisy image to the clean one and a generator mapping the clean image to the noisy one.
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
caiyuanhao1998/PNGAN
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NeurIPS 2021
Additionally, for better noise fitting, we present an efficient architecture Simple Multi-scale Network (SMNet) as the generator.
