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 501–525 of 7282 papers

TitleStatusHype
Diffusion Models as Network Optimizers: Explorations and AnalysisCode1
DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-DrivingCode1
Adaptive Unfolding Total Variation Network for Low-Light Image EnhancementCode1
Controlling Latent Diffusion Using Latent CLIPCode1
Diffusion Models for Medical Anomaly DetectionCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality AssessmentCode1
A Neural-Network-Based Convex Regularizer for Inverse ProblemsCode1
D3RM: A Discrete Denoising Diffusion Refinement Model for Piano TranscriptionCode1
A Neural Space-Time Representation for Text-to-Image PersonalizationCode1
Continual Learning of Diffusion Models with Generative DistillationCode1
Continuous Speculative Decoding for Autoregressive Image GenerationCode1
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty EstimationCode1
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image SegmentationCode1
Accelerating Diffusion Models via Early Stop of the Diffusion ProcessCode1
Content-Noise Complementary Learning for Medical Image DenoisingCode1
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned GenerationCode1
Diffusion Model as Representation LearnerCode1
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic SegmenterCode1
Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be ConsistentCode1
CL-DiffPhyCon: Closed-loop Diffusion Control of Complex Physical SystemsCode1
Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic ManipulationCode1
COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video EditingCode1
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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