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 301–325 of 7282 papers

TitleStatusHype
Dreamer XL: Towards High-Resolution Text-to-3D Generation via Trajectory Score MatchingCode2
Diffusion Models and Representation Learning: A SurveyCode2
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
Diffusion Recommender ModelCode2
Anomaly Detection with Conditioned Denoising Diffusion ModelsCode2
DynamiCtrl: Rethinking the Basic Structure and the Role of Text for High-quality Human Image AnimationCode2
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image GenerationCode2
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph DiffusionCode2
EDICT: Exact Diffusion Inversion via Coupled TransformationsCode2
Beyond Text: Frozen Large Language Models in Visual Signal ComprehensionCode2
Diffusion Bridge Implicit ModelsCode2
ExT5: Towards Extreme Multi-Task Scaling for Transfer LearningCode2
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex NoiseCode2
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized AttentionCode2
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth EstimationCode2
Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model InferenceCode2
Fast Training of Diffusion Models with Masked TransformersCode2
FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion ModelsCode2
Diffusion-based Generation, Optimization, and Planning in 3D ScenesCode2
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly DetectionCode2
FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive LearningCode2
FORA: Fast-Forward Caching in Diffusion Transformer AccelerationCode2
BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained DiffusionCode2
Diffusion-based Visual Anagram as Multi-task LearningCode2
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