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 1301–1325 of 7282 papers

TitleStatusHype
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
INCODE: Implicit Neural Conditioning with Prior Knowledge EmbeddingsCode1
Argmax Flows and Multinomial Diffusion: Learning Categorical DistributionsCode1
Decoder Denoising Pretraining for Semantic SegmentationCode1
A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large ShiftCode1
Inference-Time Text-to-Video Alignment with Diffusion Latent Beam SearchCode1
Improving Implicit Feedback-Based Recommendation through Multi-Behavior AlignmentCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class LabelsCode1
A Differentiable Perceptual Audio Metric Learned from Just Noticeable DifferencesCode1
Improving Image Restoration through Removing Degradations in Textual RepresentationsCode1
Diffusion Models as Network Optimizers: Explorations and AnalysisCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word AlignmentCode1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain ImagesCode1
Diffusion Models for Graphs Benefit From Discrete State SpacesCode1
Civil Rephrases Of Toxic Texts With Self-Supervised TransformersCode1
A CNN-Based Blind Denoising Method for Endoscopic ImagesCode1
DDT: Dual-branch Deformable Transformer for Image DenoisingCode1
A cross Transformer for image denoisingCode1
Memory AMPCode1
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising LearningCode1
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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