medical

Noise Estimation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in noise-estimation

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Libraries

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Use these libraries to find noise-estimation models and implementations

caiyuanhao1998/PNGAN

5 papers 114

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SIDD

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Most implemented papers

Unprocessing Images for Learned Raw Denoising

B. Mildenhall, J. Barron, Tianfan Xue, Tim Brooks, Jiawen Chen, Dillon Sharlet•Mon Nov 26 2018

This work presents a technique to “unprocess” images by inverting each step of an image processing pipeline, thereby allowing us to synthesize realistic raw sensor measurements from commonly available Internet photos.

500

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Toward Convolutional Blind Denoising of Real Photographs

K. Zhang, W. Zuo, Lei Zhang, Shi Guo, Zifei Yan•Wed Jul 11 2018

A convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs and a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet.

1040 0

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Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach

A. Menon, Giorgio Patrini, A. Rozza, R. Nock, Lizhen Qu•Mon Sep 12 2016

It is proved that, when ReLU is the only non-linearity, the loss curvature is immune to class-dependent label noise, and it is shown how one can estimate these probabilities, adapting a recent technique for noise estimation to the multi-class setting, and providing an end-to-end framework.

1613 0

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Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

Curtis G. Northcutt, Isaac L. Chuang, Tailin Wu•Wed May 03 2017

Rank Pruning is proposed to solve noisy PN learning and the open problem of estimating the noise rates, i.e. the fraction of wrong positive and negative labels, and is time-efficient and general.

171 0

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Automatic, fast and robust characterization of noise distributions for diffusion MRI

Samuel St-Jean, A. Luca, M. Viergever, A. Leemans•Tue May 29 2018

This work introduces a new method where a change of variable naturally gives rise to a particular form of the gamma distribution for background signals, and shows that the estimated parameters are stable and have lower variances than compared methods.

7 0

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GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-Based Real-World Noise Modeling

Seung‐Won Jung, Dong-Wook Kim, Jae‐Ryun Chung•Sun May 26 2019

A grouped residual dense network (GRDN) is proposed, which is an extended and generalized architecture of the state-of-the-art residual densenetwork (RDN) and a new generative adversarial network-based real-world noise modeling method is developed.

126 0

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Variational Denoising Network: Toward Blind Noise Modeling and Removal

Deyu Meng, Lei Zhang, Zongsheng Yue, Hongwei Yong, Qian Zhao•Wed Aug 28 2019

This work proposes a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image Denoising, and presents an approximate posterior, parameterized by deep neural networks, presented by taking the intrinsic clean image and noise variances as latent variables conditioned on the input noisy image.

358 0

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Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation

Deyu Meng, Lei Zhang, Zongsheng Yue, Qian Zhao•Sat Jul 11 2020

A novel unified framework to simultaneously deal with the noise removal and noise generation tasks, instead of only inferring the posteriori distribution of the latent clean image conditioned on the observed noisy image in traditional MAP framework, which implicitly contains all the information between the noisy and clean images.

244 0

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Pyramid Real Image Denoising Network

Yiyun Zhao, Zhuqing Jiang, Aidong Men, Guodong Ju•Wed Jul 31 2019

A novel pyramid real image denoising network (PRIDNet), which contains three stages, which uses channel attention mechanism to recalibrate the channel importance of input noise and adopts a kernel selecting operation to adaptively fuse multi-scale features.

41 0

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Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training

D. Wei, H. Pfister, Haoqian Wang, Xiaowan Hu, Yulun Zhang•Tue Apr 05 2022

Qualitative validation shows that noise generated by PNGAN is highly similar to real noise in terms of intensity and distribution, and an efficient architecture Simple Multi-scale Network (SMNet) as the generator is presented.

78 0

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