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 2376–2400 of 7282 papers

TitleStatusHype
From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance—0
Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in Medical Image Denoising—0
Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding—0
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation—0
MR imaging in the low-field: Leveraging the power of machine learning—0
Variational Schrödinger Momentum Diffusion—0
The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture ModelCode0
Decrypting the temperature field in flow boiling with latent diffusion models—0
ARFlow: Autogressive Flow with Hybrid Linear Attention—0
TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMsCode0
AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement—0
VideoPure: Diffusion-based Adversarial Purification for Video RecognitionCode0
MAP-based Problem-Agnostic diffusion model for Inverse Problems—0
Snapshot Compressed Imaging Based Single-Measurement Computer Vision for Videos—0
Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation—0
Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders—0
On the locality bias and results in the Long Range Arena—0
Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images—0
EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems—0
Binary Diffusion Probabilistic Model—0
Radio Map Estimation via Latent Domain Plug-and-Play DenoisingCode0
Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction—0
Robust Representation Consistency Model via Contrastive DenoisingCode0
Sequential Change Point Detection via Denoising Score Matching—0
FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image RestorationCode0
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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