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 601–625 of 7282 papers

TitleStatusHype
Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging—0
TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion ModelsCode1
Illuminating Darkness: Enhancing Real-world Low-light Scenes with Smartphone ImagesCode1
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—0
MIGA: Mutual Information-Guided Attack on Denoising Models for Semantic Manipulation—0
Denoising Hamiltonian Network for Physical Reasoning—0
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation—0
Graph Chirp Signal and Graph Fractional Vertex-Frequency Energy Distribution—0
Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images—0
TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation—0
PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation—0
A Light and Tuning-free Method for Simulating Camera Motion in Video GenerationCode1
One-Step Diffusion Model for Image Motion-DeblurringCode1
D3DR: Lighting-Aware Object Insertion in Gaussian Splatting—0
ProSE: Diffusion Priors for Speech Enhancement—0
Diffusion Model Based Probabilistic Day-ahead Load Forecasting—0
Federated Learning for Diffusion Models—0
Speech Audio Generation from dynamic MRI via a Knowledge Enhanced Conditional Variational Autoencoder—0
PTDiffusion: Free Lunch for Generating Optical Illusion Hidden Pictures with Phase-Transferred Diffusion ModelCode1
Explainable Synthetic Image Detection through Diffusion Timestep Ensembling—0
PointDiffuse: A Dual-Conditional Diffusion Model for Enhanced Point Cloud Semantic Segmentation—0
WaveStitch: Flexible and Fast Conditional Time Series Generation with Diffusion ModelsCode0
D2GV: Deformable 2D Gaussian Splatting for Video Representation in 400FPSCode2
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-ResolutionCode1
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous DrivingCode3
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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