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 2276–2300 of 7282 papers

TitleStatusHype
Deep priors for satellite image restoration with accurate uncertainties—0
Deep Point Set Resampling via Gradient Fields—0
Deep Point Cloud Reconstruction—0
Deep Plug-and-play Prior for Low-rank Tensor Completion—0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising—0
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks—0
EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model—0
Deep Photon Mapping—0
Adaptive Estimation and Learning under Temporal Distribution Shift—0
All-optical image denoising using a diffractive visual processor—0
Embedding models through the lens of Stable Coloring—0
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding—0
DeepPCR: Parallelizing Sequential Operations in Neural Networks—0
Balancing the Style-Content Trade-Off in Sentiment Transfer UsingPolarity-Aware Denoising—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
Emage: Non-Autoregressive Text-to-Image Generation—0
E-MD3C: Taming Masked Diffusion Transformers for Efficient Zero-Shot Object Customization—0
Deep Nonparametric Estimation of Intrinsic Data Structures by Chart Autoencoders: Generalization Error and Robustness—0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
Balancing Act: Distribution-Guided Debiasing in Diffusion Models—0
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data—0
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—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