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 5776–5800 of 7282 papers

TitleStatusHype
Variational inference of latent state sequences using Recurrent Networks—0
Conditional Denoising Diffusion for Sequential Recommendation—0
Conditional Denoising Diffusion for ISAC Enhanced Channel Estimation in Cell-Free 6G—0
Conditional Denoising of Remote Sensing Imagery Using Cycle-Consistent Deep Generative Models—0
Denoising-Contrastive Alignment for Continuous Sign Language Recognition—0
Conditional expectation using compactification operators—0
Conditional GAN for Enhancing Diffusion Models in Efficient and Authentic Global Gesture Generation from Audios—0
Conditional Generation of Temporally-ordered Event Sequences—0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
An Approach for Reducing Outliers of Non Local Means Image Denoising Filter—0
Conditional Lagrangian Wasserstein Flow for Time Series Imputation—0
Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders—0
Sophisticated deep learning with on-chip optical diffractive tensor processing—0
Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation—0
Variational Inference using Implicit Distributions—0
Conjugate Gradient Acceleration of Non-Linear Smoothing Filters—0
Connecting Supervised and Unsupervised Sentence Embeddings—0
Con-Patch: When a Patch Meets its Context—0
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples—0
Conservativeness of untied auto-encoders—0
Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems—0
Consistency analysis of bilevel data-driven learning in inverse problems—0
Sources of Noise in Dialogue and How to Deal with Them—0
Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness—0
An Analysis on Quantizing Diffusion Transformers—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