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

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Showing 476–500 of 7282 papers

TitleStatusHype
An Analysis and Implementation of the HDR+ Burst Denoising MethodCode1
DiffSim: Taming Diffusion Models for Evaluating Visual SimilarityCode1
DiffSF: Diffusion Models for Scene Flow EstimationCode1
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion ModelsCode1
Diff-Reg v1: Diffusion Matching Model for Registration ProblemCode1
1st Place Solution for the 5th LSVOS Challenge: Video Instance SegmentationCode1
DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintsCode1
DiffStyler: Controllable Dual Diffusion for Text-Driven Image StylizationCode1
DiffProsody: Diffusion-based Latent Prosody Generation for Expressive Speech Synthesis with Prosody Conditional Adversarial TrainingCode1
ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion ProcessCode1
Diffusion Bridge: Leveraging Diffusion Model to Reduce the Modality Gap Between Text and Vision for Zero-Shot Image CaptioningCode1
3D Detection and Characterisation of ALMA Sources through Deep LearningCode1
DiffQRCoder: Diffusion-based Aesthetic QR Code Generation with Scanning Robustness Guided Iterative RefinementCode1
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
Adaptive Unfolding Total Variation Network for Low-Light Image EnhancementCode1
DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View SynthesisCode1
DiffStyler: Diffusion-based Localized Image Style TransferCode1
Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series ForecastingCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman ProblemCode1
Diffusion Model for Dense MatchingCode1
Empowering Diffusion Models on the Embedding Space for Text GenerationCode1
Diffusion Models Beat GANs on Image ClassificationCode1
DiffO: Single-step Diffusion for Image Compression at Ultra-Low BitratesCode1
Accelerating Diffusion Models via Early Stop of the Diffusion ProcessCode1
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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