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 4001–4025 of 7282 papers

TitleStatusHype
Generative Joint Source-Channel Coding for Semantic Image Transmission—0
Generative Lifting of Multiview to 3D from Unknown Pose: Wrapping NeRF inside Diffusion—0
Generative Lines Matching Models—0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—0
Generative Model for Heterogeneous Inference—0
TDM: Temporally-Consistent Diffusion Model for All-in-One Real-World Video Restoration—0
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling—0
Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection—0
"Generative Models for Financial Time Series Data: Enhancing Signal-to-Noise Ratio and Addressing Data Scarcity in A-Share Market—0
Generative Models for Low-Dimensional Video Representation and Compressive Sensing—0
Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study—0
Generative Neural Fields by Mixtures of Neural Implicit Functions—0
Generative Precipitation Downscaling using Score-based Diffusion with Wasserstein Regularization—0
VividDreamer: Towards High-Fidelity and Efficient Text-to-3D Generation—0
Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Leveraging Color Shift Correction, RoPE-Swin Backbone, and Quantile-based Label Denoising Strategy for Robust Outdoor Scene Understanding—0
Generative Pseudo-Inverse Memory—0
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising—0
Generative Recommendation with Continuous-Token Diffusion—0
Generative Speech Foundation Model Pretraining for High-Quality Speech Extraction and Restoration—0
Generative thermodynamic computing—0
Adversarial purification for no-reference image-quality metrics: applicability study and new methods—0
Generic 3D Convolutional Fusion for image restoration—0
GenesisTex: Adapting Image Denoising Diffusion to Texture Space—0
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation—0
Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise—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