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 2726–2750 of 7282 papers

TitleStatusHype
A Simple Yet Effective Improvement to the Bilateral Filter for Image Denoising—0
A 51.3 TOPS/W, 134.4 GOPS In-memory Binary Image Filtering in 65nm CMOS—0
CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion—0
A Simple Sparse Denoising Layer for Robust Deep Learning—0
Fréchet regression with implicit denoising and multicollinearity reduction—0
CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step—0
Exploring Diffusion with Test-Time Training on Efficient Image Restoration—0
A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter—0
A Fokker-Planck-Based Loss Function that Bridges Dynamics with Density Estimation—0
Exploring Continual Learning of Diffusion Models—0
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer—0
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina—0
Foundation Cures Personalization: Recovering Facial Personalized Models' Prompt Consistency—0
FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion—0
Exploring Attribute Variations in Style-based GANs using Diffusion Models—0
Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios—0
Cost-Aware Routing for Efficient Text-To-Image Generation—0
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving—0
Explorative Inbetweening of Time and Space—0
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization—0
A Flow-based Truncated Denoising Diffusion Model for Super-resolution Magnetic Resonance Spectroscopic Imaging—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.—0
Exploring ensembles and uncertainty minimization in denoising networks—0
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness—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