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 2501–2525 of 7282 papers

TitleStatusHype
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient NetworkCode0
Hybrid Spatial-spectral Neural Network for Hyperspectral Image DenoisingCode0
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising ModelCode0
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited DataCode0
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
Domain-Adversarial Neural NetworksCode0
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion ModelsCode0
Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String SimilaritiesCode0
Efficient and Parallel Separable Dictionary LearningCode0
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural NetworkCode0
How Control Information Influences Multilingual Text Image Generation and Editing?Code0
Holistic Guidance for Occluded Person Re-IdentificationCode0
An Underparametrized Deep Decoder Architecture for Graph SignalsCode0
How Does Diffusion Influence Pretrained Language Models on Out-of-Distribution Data?Code0
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder ApproachCode0
RDSA: A Robust Deep Graph Clustering Framework via Dual Soft AssignmentCode0
Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNNCode0
Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion modelCode0
High-dimensional Assisted Generative Model for Color Image RestorationCode0
Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based RecommendationCode0
ColorFool: Semantic Adversarial ColorizationCode0
Hierarchical Relational Networks for Group Activity Recognition and RetrievalCode0
Hiding Images in Diffusion Models by Editing Learned Score FunctionsCode0
HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG DenoisingCode0
Show:102550
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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