Image Denoising

379 papers with code • 19 benchmarks • 17 datasets

Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.

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


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Most implemented papers

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.

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.

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.

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.

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.

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.

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.

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.

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.

Simple Baselines for Image Restoration

megvii-research/NAFNet 10 Apr 2022

Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods.