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 5951–6000 of 7282 papers

TitleStatusHype
A Comparative Study of Image Denoising Algorithms—0
Unsupervised Anomaly Detection Using Diffusion Trend Analysis—0
A Comparative Study of Filtering Approaches Applied to Color Archival Document Images—0
Removing Adversarial Noise in Class Activation Feature Space—0
Removing Anomalies as Noises for Industrial Defect Localization—0
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation—0
Removing Rain From Single Images via a Deep Detail Network—0
A Comparative Study for the Nuclear Norms Minimization Methods—0
A Comparative Evaluation of Deep Learning Models for Speech Enhancement in Real-World Noisy Environments—0
Unsupervised Coordinate-Based Video Denoising—0
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation—0
Representation, Analysis of Bayesian Refinement Approximation Network: A Survey—0
Representation Learning based and Interpretable Reactor System Diagnosis Using Denoising Padded Autoencoder—0
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement—0
Representation Learning for Resource-Constrained Keyphrase Generation—0
Unsupervised CP-UNet Framework for Denoising DAS Data with Decay Noise—0
Representation Learning for Spatial Graphs—0
Diffusion-Based Representation Learning—0
Representation Learning in Continuous-Time Score-Based Generative Models—0
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising—0
Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Example—0
Representing and Denoising Wearable ECG Recordings—0
Polar Encoding: A Simple Baseline Approach for Classification with Missing Values—0
WISVA: Generative AI for 5G Network Optimization in Smart Warehouses—0
ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning—0
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution—0
Unsupervised Denoising for Satellite Imagery using Wavelet Subband CycleGAN—0
Unsupervised denoising for sparse multi-spectral computed tomography—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategy—0
Residual Transformer Fusion Network for Salt and Pepper Image Denoising—0
Resilient Sparse Array Radar with the Aid of Deep Learning—0
ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance—0
Resolution Chromatography of Diffusion Models—0
Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation—0
Response Matching for generating materials and molecules—0
Restoration by Generation with Constrained Priors—0
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers—0
Restoration Score Distillation: From Corrupted Diffusion Pretraining to One-Step High-Quality Generation—0
Unsupervised Denoising of Optical Coherence Tomography Images with Dual_Merged CycleWGAN—0
Restore from Restored: Single Image Denoising with Pseudo Clean Image—0
Restore from Restored: Video Restoration with Pseudo Clean Video—0
RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior—0
Accurate Graph Filtering in Wireless Sensor Networks—0
RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration—0
RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems—0
Restoring Real-World Images with an Internal Detail Enhancement Diffusion Model—0
Restoring STM images via Sparse Coding: noise and artifact removal—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
Zero-Shot Paraphrase Generation with Multilingual Language Models—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