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 401–425 of 7282 papers

TitleStatusHype
ProcessPainter: Learn Painting Process from Sequence DataCode2
Diffusion Models and Representation Learning: A SurveyCode2
Diffusion models as plug-and-play priorsCode2
PromptIR: Prompting for All-in-One Blind Image RestorationCode2
Anomaly Detection with Conditioned Denoising Diffusion ModelsCode2
DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion ModelsCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
Real-World Mobile Image Denoising Dataset with Efficient BaselinesCode2
Reconstructive Visual Instruction TuningCode2
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion ModelsCode2
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex NoiseCode2
Aligning Text-to-Image Diffusion Models with Reward BackpropagationCode2
DiffusionInst: Diffusion Model for Instance SegmentationCode2
A DeNoising FPN With Transformer R-CNN for Tiny Object DetectionCode2
Diffusion Bridge Implicit ModelsCode2
Diffusion-based Visual Anagram as Multi-task LearningCode2
DeFoG: Discrete Flow Matching for Graph GenerationCode2
RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow MatchingCode2
Diffusion-based Generation, Optimization, and Planning in 3D ScenesCode2
DiffusionBERT: Improving Generative Masked Language Models with Diffusion ModelsCode2
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse AutoencodersCode2
SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video GenerationCode2
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth EstimationCode2
Diffusion^2: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion ModelsCode2
Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion ModelsCode2
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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