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 6276–6300 of 7282 papers

TitleStatusHype
SPI-GAN: Denoising Diffusion GANs with Straight-Path Interpolations—0
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions—0
Denoising Multi-modal Sequential Recommenders with Contrastive Learning—0
Spike Stream Denoising via Spike Camera Simulation—0
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning—0
Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning—0
Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection—0
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback—0
Denoising neural networks for magnetic resonance spectroscopy—0
Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference—0
Spiking sampling network for image sparse representation and dynamic vision sensor data compression—0
Denoising of discrete-time chaotic signals using echo state networks—0
Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction—0
Denoising of image gradients and total generalized variation denoising—0
Denoising of photogrammetric dummy head ear point clouds for individual Head-Related Transfer Functions computation—0
Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of Convolution Neural Network—0
Denoising Opponents Position in Partial Observation Environment—0
Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models—0
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation—0
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers—0
Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor—0
Denoising random forests—0
AAMDM: Accelerated Auto-regressive Motion Diffusion Model—0
Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Latent Generation—0
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality 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