SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5151–5175 of 7282 papers

TitleStatusHype
Manifold Learning Benefits GANs—0
Self-Attention Generative Adversarial Network for Iterative Reconstruction of CT Images—0
Exploring Inter-frequency Guidance of Image for Lightweight Gaussian DenoisingCode0
Are Large-scale Datasets Necessary for Self-Supervised Pre-training?—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
Multimeasurement Generative ModelsCode0
Heavy-tailed denoising score matchingCode0
SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers—0
Procedural Kernel Networks—0
All You Need is RAW: Defending Against Adversarial Attacks with Camera Image PipelinesCode0
A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory—0
Supervised learning of analysis-sparsity priors with automatic differentiation—0
CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising—0
EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation—0
N-SfC: Robust and Fast Shape Estimation from Caustic Images—0
More Control for Free! Image Synthesis with Semantic Diffusion Guidance—0
Superpixel-Based Building Damage Detection from Post-earthquake Imagery Using Deep Neural Networks—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection—0
A comparison study of CNN denoisers on PRNU extraction—0
A Survey on Deep learning based Document Image Enhancement—0
Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching—0
Snapshot HDR Video Construction Using Coded Mask—0
Efficient joint noise removal and multi exposure fusion—0
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified