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 176–200 of 7282 papers

TitleStatusHype
STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation—0
InstGenIE: Generative Image Editing Made Efficient with Mask-aware Caching and Scheduling—0
Be Decisive: Noise-Induced Layouts for Multi-Subject Generation—0
Hume: Introducing System-2 Thinking in Visual-Language-Action Model—0
Leveraging Diffusion Models for Parameterized Quantum Circuit Generation—0
Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion—0
Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations—0
Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective ApproachCode1
Rotation-Equivariant Self-Supervised Method in Image DenoisingCode1
DiSA: Diffusion Step Annealing in Autoregressive Image GenerationCode2
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space—0
MotionPro: A Precise Motion Controller for Image-to-Video Generation—0
Structure Disruption: Subverting Malicious Diffusion-Based Inpainting via Self-Attention Query Perturbation—0
Multimodal LLM-Guided Semantic Correction in Text-to-Image DiffusionCode1
Deep Spectral Prior—0
Training-Free Multi-Step Audio Source SeparationCode2
ICDM: Interference Cancellation Diffusion Models for Wireless Semantic Communications—0
Refining Few-Step Text-to-Multiview Diffusion via Reinforcement LearningCode0
Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning—0
Exploring Magnitude Preservation and Rotation Modulation in Diffusion Transformers—0
Step-level Reward for Free in RL-based T2I Diffusion Model Fine-tuningCode1
VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion—0
Test-Time Scaling of Diffusion Models via Noise Trajectory SearchCode0
SW-ViT: A Spatio-Temporal Vision Transformer Network with Post Denoiser for Sequential Multi-Push Ultrasound Shear Wave Elastography—0
Image denoising as a conditional expectation—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