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 4851–4900 of 7282 papers

TitleStatusHype
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
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models—0
ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder—0
Rethinking Circuit Completeness in Language Models: AND, OR, and ADDER Gates—0
Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise—0
Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data—0
Unsupervised Domain Adaptation for ToF Data Denoising With Adversarial Learning—0
Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder—0
Unsupervised Fingerphoto Presentation Attack Detection With Diffusion Models—0
Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction—0
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction—0
Rethinking Weak Supervision in Helping Contrastive Learning—0
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection—0
Retinal OCT Denoising with Pseudo-Multimodal Fusion Network—0
Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation—0
Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer—0
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework—0
Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior—0
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack—0
Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization—0
ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization—0
Retrieval Term Prediction Using Deep Learning Methods—0
Revealing Directions for Text-guided 3D Face Editing—0
Revealing higher-order neural representations of uncertainty with the Noise Estimation through Reinforcement-based Diffusion (NERD) model—0
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report—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