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 1851–1875 of 7282 papers

TitleStatusHype
A Composite Predictive-Generative Approach to Monaural Universal Speech Enhancement—0
TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models—0
LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers—0
Enhanced DACER Algorithm with High Diffusion Efficiency—0
ZeroSep: Separate Anything in Audio with Zero Training—0
GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation—0
LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter—0
Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple SclerosisCode0
WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver-Assistance Systems—0
Composite Reward Design in PPO-Driven Adaptive FilteringCode0
Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample FilteringCode0
ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning—0
Leveraging Diffusion Models for Synthetic Data Augmentation in Protein Subcellular Localization Classification—0
PanoWan: Lifting Diffusion Video Generation Models to 360° with Latitude/Longitude-aware Mechanisms—0
Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models—0
D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples—0
GLAMP: An Approximate Message Passing Framework for Transfer Learning with Applications to Lasso-based Estimators—0
Algorithm Unrolling-based Denoising of Multimodal Graph Signals—0
Plug-and-Play Posterior Sampling for Blind Inverse Problems—0
ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning—0
Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study—0
Shapley Value-driven Data Pruning for Recommender SystemsCode0
Rhetorical Text-to-Image Generation via Two-layer Diffusion Policy Optimization—0
MoE-Gyro: Self-Supervised Over-Range Reconstruction and Denoising for MEMS Gyroscopes—0
Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion—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