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 326–350 of 7282 papers

TitleStatusHype
Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T TrackingCode0
MRI motion correction via efficient residual-guided denoising diffusion probabilistic models—0
FlexiAct: Towards Flexible Action Control in Heterogeneous Scenarios—0
Dual Prompting for Diverse Count-level PET Denoising—0
Multi-View Learning with Context-Guided Receptance for Image DenoisingCode1
Quantizing Diffusion Models from a Sampling-Aware Perspective—0
DualReal: Adaptive Joint Training for Lossless Identity-Motion Fusion in Video Customization—0
Enhancing Black-Litterman Portfolio via Hybrid Forecasting Model Combining Multivariate Decomposition and Noise Reduction—0
Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing—0
Generative diffusion model surrogates for mechanistic agent-based biological models—0
A Time-Series Data Augmentation Model through Diffusion and Transformer Integration—0
AI-Driven Segmentation and Analysis of Microbial Cells—0
Safety-Critical Traffic Simulation with Guided Latent Diffusion Model—0
Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution—0
GarmentDiffusion: 3D Garment Sewing Pattern Generation with Multimodal Diffusion Transformers—0
MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric GuidanceCode0
Noise Modeling in One Hour: Minimizing Preparation Efforts for Self-supervised Low-Light RAW Image DenoisingCode2
Adept: Annotation-Denoising Auxiliary Tasks with Discrete Cosine Transform Map and Keypoint for Human-Centric Pretraining—0
PixelHacker: Image Inpainting with Structural and Semantic ConsistencyCode3
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound NoiseCode1
DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models—0
HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models—0
Generative AI for Physical-Layer Authentication—0
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup FunctionalCode1
Outlier-aware Tensor Robust Principal Component Analysis with Self-guided Data Augmentation—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