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 801–825 of 7282 papers

TitleStatusHype
TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMsCode0
AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement—0
VideoPure: Diffusion-based Adversarial Purification for Video RecognitionCode0
Snapshot Compressed Imaging Based Single-Measurement Computer Vision for Videos—0
MAP-based Problem-Agnostic diffusion model for Inverse Problems—0
On the locality bias and results in the Long Range Arena—0
Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation—0
Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images—0
Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders—0
VideoShield: Regulating Diffusion-based Video Generation Models via WatermarkingCode2
EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems—0
An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman ProblemCode1
Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction—0
Radio Map Estimation via Latent Domain Plug-and-Play DenoisingCode0
Binary Diffusion Probabilistic Model—0
Self-Supervised Diffusion MRI Denoising via Iterative and Stable RefinementCode1
TeD-Loc: Text Distillation for Weakly Supervised Object LocalizationCode0
Robust Representation Consistency Model via Contrastive DenoisingCode0
Accelerate High-Quality Diffusion Models with Inner Loop Feedback—0
FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image RestorationCode0
Sequential Change Point Detection via Denoising Score Matching—0
Graph Representation Learning with Diffusion Generative Models—0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
Low-dimensional adaptation of diffusion models: Convergence in total variation—0
Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling—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