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

Showing 451475 of 7282 papers

TitleStatusHype
FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video GenerationCode1
TracLLM: A Generic Framework for Attributing Long Context LLMsCode1
Adaptive Differential Denoising for Respiratory Sounds ClassificationCode1
Unleashing High-Quality Image Generation in Diffusion Sampling Using Second-Order Levenberg-Marquardt-LangevinCode1
ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory ImputationCode1
Minute-Long Videos with Dual ParallelismsCode1
Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective ApproachCode1
Multimodal LLM-Guided Semantic Correction in Text-to-Image DiffusionCode1
Rotation-Equivariant Self-Supervised Method in Image DenoisingCode1
Step-level Reward for Free in RL-based T2I Diffusion Model Fine-tuningCode1
Guided Diffusion Sampling on Function Spaces with Applications to PDEsCode1
REPA Works Until It Doesn't: Early-Stopped, Holistic Alignment Supercharges Diffusion TrainingCode1
OSCAR: One-Step Diffusion Codec for Image Compression Across Multiple Bit-ratesCode1
DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation TrainerCode1
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
FlowPure: Continuous Normalizing Flows for Adversarial PurificationCode1
Video-GPT via Next Clip DiffusionCode1
DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion ModelCode1
Total Variation-Based Image Decomposition and Denoising for Microscopy ImagesCode1
TS-Diff: Two-Stage Diffusion Model for Low-Light RAW Image EnhancementCode1
Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation AbilityCode1
Multi-View Learning with Context-Guided Receptance for Image DenoisingCode1
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound NoiseCode1
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup FunctionalCode1
Physics-guided and fabrication-aware inverse design of photonic devices using diffusion modelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified