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 1551–1575 of 7282 papers

TitleStatusHype
EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidanceCode2
Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation—0
Localized Schrödinger Bridge Sampler—0
Quaternion Nuclear Norm minus Frobenius Norm Minimization for color image reconstruction—0
LoRID: Low-Rank Iterative Diffusion for Adversarial Purification—0
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models—0
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals—0
Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models—0
FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process—0
Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image EnhancementCode1
Structure Modeling Activation Free Fourier Network for Spacecraft Image DenoisingCode0
Exploring User-level Gradient Inversion with a Diffusion Prior—0
Realistic and Efficient Face Swapping: A Unified Approach with Diffusion ModelsCode2
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning—0
DiffQRCoder: Diffusion-based Aesthetic QR Code Generation with Scanning Robustness Guided Iterative RefinementCode1
Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer—0
A Practical Gated Recurrent Transformer Network Incorporating Multiple Fusions for Video Denoising—0
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty QuantificationCode0
IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTSCode2
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning—0
Dual convolutional neural network with attention for image blind denoisingCode1
Physics-augmented Deep Learning with Adversarial Domain Adaptation: Applications to STM Image Denoising—0
STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting—0
Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries—0
Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis—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