Color Image Denoising

32 papers with code • 80 benchmarks • 9 datasets

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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.

Residual Dense Network for Image Super-Resolution

yulunzhang/RDN CVPR 2018

In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers.

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.

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.

FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising

cszn/FFDNet 11 Oct 2017

Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising.

Pre-Trained Image Processing Transformer

huawei-noah/Pretrained-IPT CVPR 2021

To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.

Real Image Denoising with Feature Attention

saeed-anwar/RIDNet ICCV 2019

Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling.

Improving Image Restoration by Revisiting Global Information Aggregation

megvii-research/TLC 8 Dec 2021

Our TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images.

Learning Deep CNN Denoiser Prior for Image Restoration

cszn/ircnn CVPR 2017

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).

MemNet: A Persistent Memory Network for Image Restoration

tyshiwo/MemNet ICCV 2017

We apply MemNet to three image restoration tasks, i. e., image denosing, super-resolution and JPEG deblocking.