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 251–275 of 7282 papers

TitleStatusHype
SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image DenoisingCode2
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph DiffusionCode2
LocInv: Localization-aware Inversion for Text-Guided Image EditingCode2
TheaterGen: Character Management with LLM for Consistent Multi-turn Image GenerationCode2
TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion ModelsCode2
CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement TaskCode2
Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noiseCode2
Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image DenoisingCode2
Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion ModelsCode2
DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR ImagesCode2
Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image RestorationCode2
Diffusion^2: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion ModelsCode2
Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic PropagationCode2
Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-OnCode2
CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency ModelsCode2
Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image InpaintingCode2
BAMM: Bidirectional Autoregressive Motion ModelCode2
SingularTrajectory: Universal Trajectory Predictor Using Diffusion ModelCode2
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion ModelCode2
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image GenerationCode2
Transfer CLIP for Generalizable Image DenoisingCode2
SyncTweedies: A General Generative Framework Based on Synchronized DiffusionsCode2
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly DetectionCode2
Tuning-Free Image Customization with Image and Text GuidanceCode2
You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANsCode2
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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