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 3676–3700 of 7282 papers

TitleStatusHype
Explainable Synthetic Image Detection through Diffusion Timestep Ensembling—0
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data—0
SwinIA: Self-Supervised Blind-Spot Image Denoising without Convolutions—0
Explicit Diffusion of Gaussian Mixture Model Based Image Priors—0
Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint—0
Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning—0
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning—0
Exploiting network optimization stability for enhanced PET image denoising using deep image prior—0
Exploiting Temporal Attention Features for Effective Denoising in Videos—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model—0
Explorative Inbetweening of Time and Space—0
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving—0
Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios—0
Exploring Attribute Variations in Style-based GANs using Diffusion Models—0
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer—0
Exploring Continual Learning of Diffusion Models—0
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization—0
3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model—0
Exploring Diffusion with Test-Time Training on Efficient Image Restoration—0
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.—0
Exploring ensembles and uncertainty minimization in denoising networks—0
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms—0
Exploring Magnitude Preservation and Rotation Modulation in Diffusion Transformers—0
Exploring Optimal Latent Trajetory for Zero-shot Image Editing—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