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 6401–6425 of 7282 papers

TitleStatusHype
Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman SpectraCode0
Language Embeddings for Typology and Cross-lingual Transfer LearningCode0
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative RefinementCode0
Language-Guided Diffusion Model for Visual GroundingCode0
Fully Unsupervised Probabilistic Noise2VoidCode0
Language Model Preference Evaluation with Multiple Weak EvaluatorsCode0
Periodic Materials Generation using Text-Guided Joint Diffusion ModelCode0
Zero-Shot Enhancement of Low-Light Image Based on Retinex DecompositionCode0
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds DefenseCode0
Fuse Your Latents: Video Editing with Multi-source Latent Diffusion ModelsCode0
A Semi-Supervised Approach for Low-Resourced Text GenerationCode0
A Showcase of the Use of Autoencoders in Feature Learning ApplicationsCode0
Large Graph Signal Denoising with Application to Differential PrivacyCode0
Personalized Denoising Implicit Feedback for Robust Recommender SystemCode0
A multimodal dynamical variational autoencoder for audiovisual speech representation learningCode0
Removal of speckle noises from ultrasound images using five different deep learning networksCode0
Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning ApproachCode0
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image DenoisingCode0
Deep Graph-Convolutional Image DenoisingCode0
Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising AutoencodersCode0
Conditioning diffusion models by explicit forward-backward bridgingCode0
Game Theory for Adversarial Attacks and DefensesCode0
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy ImagesCode0
Removing Radio Frequency Interference from Auroral Kilometric Radiation with Stacked AutoencodersCode0
Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified RobustnessCode0
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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