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 3351–3375 of 7282 papers

TitleStatusHype
Conditioning diffusion models by explicit forward-backward bridgingCode0
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic ModelsCode0
Directly Denoising Diffusion Models—0
Prompt Mixing in Diffusion Models using the Black Scholes AlgorithmCode0
DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech TranslationCode0
DARK: Denoising, Amplification, Restoration KitCode0
Hybrid Digital-Analog Semantic Communications—0
Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators—0
Physics-aware Hand-object Interaction Denoising—0
Double Correction Framework for Denoising RecommendationCode0
TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy—0
Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion—0
VisioBlend: Sketch and Stroke-Guided Denoising Diffusion Probabilistic Model for Realistic Image Generation—0
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning—0
Response Matching for generating materials and molecules—0
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models—0
SAR Image Synthesis with Diffusion Models—0
FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival—0
Do Bayesian imaging methods report trustworthy probabilities?—0
Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic Trajectory Prediction via Energy Plan Denoising—0
Input Snapshots Fusion for Scalable Discrete Dynamic Graph Nerual Networks—0
Deep MMD Gradient Flow without adversarial training—0
Mesh Denoising Transformer—0
Compression-Realized Deep Structural Network for Video Quality Enhancement—0
StableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework—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