1055 papers with code • 3 benchmarks • 18 datasets

Denoising is the task of removing noise from an image.

( Image credit: Beyond a Gaussian Denoiser )


Use these libraries to find Denoising models and implementations

Most implemented papers

Denoising Diffusion Probabilistic Models

hojonathanho/diffusion NeurIPS 2020

We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.

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

huggingface/transformers ACL 2020

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.

Noise2Noise: Learning Image Restoration without Clean Data

NVlabs/noise2noise ICML 2018

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.

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

titu1994/Image-Super-Resolution 29 Jun 2016

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.

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

cszn/DnCNN 13 Aug 2016

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

Learning to See in the Dark

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

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

Denoising Diffusion Implicit Models

ermongroup/ddim ICLR 2021

Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample.

Learning Enriched Features for Real Image Restoration and Enhancement

swz30/MIRNet ECCV 2020

With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing.

High-Resolution Image Synthesis with Latent Diffusion Models

compvis/latent-diffusion CVPR 2022

By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond.

Deep Image Prior

DmitryUlyanov/deep-image-prior CVPR 2018

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.