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 201–225 of 7282 papers

TitleStatusHype
Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its MiscibilityCode2
Restoring Real-World Images with an Internal Detail Enhancement Diffusion Model—0
On Denoising Walking Videos for Gait Recognition—0
CIM-NET: A Video Denoising Deep Neural Network Model Optimized for Computing-in-Memory Architectures—0
Applications of Modular Co-Design for De Novo 3D Molecule Generation—0
Towards more transferable adversarial attack in black-box manner—0
RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration—0
Brightness-Invariant Tracking Estimation in Tagged MRI—0
CONCORD: Concept-Informed Diffusion for Dataset DistillationCode0
SHaDe: Compact and Consistent Dynamic 3D Reconstruction via Tri-Plane Deformation and Latent Diffusion—0
TRAIL: Transferable Robust Adversarial Images via Latent diffusion—0
Training-Free Efficient Video Generation via Dynamic Token CarvingCode3
REPA Works Until It Doesn't: Early-Stopped, Holistic Alignment Supercharges Diffusion TrainingCode1
Consistent World Models via Foresight Diffusion—0
Creatively Upscaling Images with Global-Regional Priors—0
Guided Diffusion Sampling on Function Spaces with Applications to PDEsCode1
Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction—0
Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation—0
Joint Flow And Feature Refinement Using Attention For Video Restoration—0
OSCAR: One-Step Diffusion Codec for Image Compression Across Multiple Bit-ratesCode1
Sufficient conditions for offline reactivation in recurrent neural networksCode0
Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning—0
Toward Theoretical Insights into Diffusion Trajectory Distillation via Operator Merging—0
dKV-Cache: The Cache for Diffusion Language ModelsCode2
Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRI—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