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 1951–1975 of 7282 papers

TitleStatusHype
Diffusion Models in De Novo Drug Design—0
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement—0
SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style TransferCode0
DiffusionPID: Interpreting Diffusion via Partial Information Decomposition—0
GenzIQA: Generalized Image Quality Assessment using Prompt-Guided Latent Diffusion Models—0
Denoising-Aware Contrastive Learning for Noisy Time SeriesCode1
Zero-Shot Video Editing through Adaptive Sliding Score Distillation—0
Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion ModelsCode2
Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential EquationsCode1
Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference OptimizationCode3
SF-V: Single Forward Video Generation ModelCode2
M&M VTO: Multi-Garment Virtual Try-On and EditingCode7
VideoTetris: Towards Compositional Text-to-Video GenerationCode3
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean DataCode2
ReDistill: Residual Encoded Distillation for Peak Memory Reduction—0
Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal RepresentationCode0
Coarse-To-Fine Tensor Trains for Compact Visual RepresentationsCode1
Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural NetworksCode1
TIDMAD: Time Series Dataset for Discovering Dark Matter with AI DenoisingCode1
RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization—0
SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade SensorsCode0
Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive DiffusionCode2
DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR ImagesCode4
A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—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