Color Image Denoising

18 papers with code • 47 benchmarks • 8 datasets

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Most implemented papers

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 18 Nov 2021

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.

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.

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.

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.

Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

majedelhelou/BUIFD 5 Jul 2019

Blind and universal image denoising consists of using a unique model that denoises images with any level of noise.

RENOIR - A Dataset for Real Low-Light Image Noise Reduction

Aftaab99/DenoisingAutoencoder 29 Sep 2014

Image denoising algorithms are evaluated using images corrupted by artificial noise, which may lead to incorrect conclusions about their performances on real noise.

Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies

tum-vision/sublabel_relax 7 Apr 2016

Convex relaxations of nonconvex multilabel problems have been demonstrated to produce superior (provably optimal or near-optimal) solutions to a variety of classical computer vision problems.