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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 526–550 of 7282 papers

TitleStatusHype
Controlling Latent Diffusion Using Latent CLIPCode1
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh ReconstructionCode1
Convolutional Proximal Neural Networks and Plug-and-Play AlgorithmsCode1
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
DiffusionVID: Denoising Object Boxes with Spatio-temporal Conditioning for Video Object DetectionCode1
DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic LanguagesCode1
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain ImagesCode1
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
Diffusion Models for Graphs Benefit From Discrete State SpacesCode1
AMP-Net: Denoising based Deep Unfolding for Compressive Image SensingCode1
Diffusion Models for Black-Box OptimizationCode1
Diffusion Models for Constrained DomainsCode1
Diffusion Models for Medical Anomaly DetectionCode1
Continual Learning of Diffusion Models with Generative DistillationCode1
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image SegmentationCode1
A Mixture-Based Framework for Guiding Diffusion ModelsCode1
Content-Noise Complementary Learning for Medical Image DenoisingCode1
Continuous Speculative Decoding for Autoregressive Image GenerationCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be ConsistentCode1
Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty EstimationCode1
Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet ExtractionCode1
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic SegmenterCode1
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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