Image Denoising

288 papers with code • 11 benchmarks • 15 datasets

Image Denoising is the task of removing noise from an image, e.g. the application of Gaussian noise to an image.

( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )


Use these libraries to find Image Denoising models and implementations
5 papers
4 papers
4 papers
See all 6 libraries.

Most implemented papers

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.

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.

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.

Restormer: Efficient Transformer for High-Resolution Image Restoration

swz30/restormer CVPR 2022

Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

yyqqss09/ldct_denoising 3 Aug 2017

In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.

CycleISP: Real Image Restoration via Improved Data Synthesis

swz30/CycleISP CVPR 2020

This is mainly because the AWGN is not adequate for modeling the real camera noise which is signal-dependent and heavily transformed by the camera imaging pipeline.

Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images

TaoHuang2018/Neighbor2Neighbor CVPR 2021

In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images.

SwinIR: Image Restoration Using Swin Transformer

jingyunliang/swinir 23 Aug 2021

In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.