Denoising

344 papers with code · Computer Vision

Denoising is the task of removing noise from an image.

( Image credit: Beyond a Gaussian Denoiser )

Benchmarks

Greatest papers with code

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

ACL 2020 huggingface/transformers

We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.

DENOISING MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING TEXT GENERATION

Unprocessing Images for Learned Raw Denoising

CVPR 2019 google-research/google-research

Machine learning techniques work best when the data used for training resembles the data used for evaluation.

IMAGE DENOISING

Deep Image Prior

CVPR 2018 DmitryUlyanov/deep-image-prior

In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.

IMAGE DENOISING IMAGE INPAINTING IMAGE RESTORATION JPEG COMPRESSION ARTIFACT REDUCTION SUPER-RESOLUTION

XLNet: Generalized Autoregressive Pretraining for Language Understanding

NeurIPS 2019 zihangdai/xlnet

With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.

DENOISING DOCUMENT RANKING LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SENTIMENT ANALYSIS

Learning to See in the Dark

CVPR 2018 cchen156/Learning-to-See-in-the-Dark

Imaging in low light is challenging due to low photon count and low SNR.

DEBLURRING DENOISING

Noise2Noise: Learning Image Restoration without Clean Data

ICML 2018 NVlabs/noise2noise

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.

DENOISING IMAGE RESTORATION

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

13 Aug 2016cszn/DnCNN

Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.

IMAGE DENOISING IMAGE SUPER-RESOLUTION

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

29 Jun 2016titu1994/Image-Super-Resolution

In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.

IMAGE DENOISING IMAGE RESTORATION SUPER-RESOLUTION

A Wavenet for Speech Denoising

ICASSP 2018 2017 drethage/speech-denoising-wavenet

In order to overcome this limitation, we propose an end-to-end learning method for speech denoising based on Wavenet.

DENOISING

Learning Deep CNN Denoiser Prior for Image Restoration

CVPR 2017 cszn/ircnn

Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).

DEBLURRING IMAGE DENOISING IMAGE RESTORATION