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 2301–2325 of 7282 papers

TitleStatusHype
A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic SegmentationCode0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
A Self-Supervised Method for Attenuating Seismic Random and Tracewise Coherent Noise under the Non-Pixelwise Independence AssumptionCode0
Convolutional Dictionary Learning: Acceleration and ConvergenceCode0
IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target RecognitionCode0
Convolutional Deblurring for Natural ImagingCode0
Incomplete Gamma Kernels: Generalizing Locally Optimal Projection OperatorsCode0
Index NetworkCode0
Inference-Time Diffusion Model DistillationCode0
Invertible generative models for inverse problems: mitigating representation error and dataset biasCode0
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
Improving Robustness to Model Inversion Attacks via Sparse Coding ArchitecturesCode0
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMCode0
Convexified Convolutional Neural NetworksCode0
Improving Hypernymy Extraction with Distributional Semantic ClassesCode0
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion ModelsCode0
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled DataCode0
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-TuningCode0
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex PenaltiesCode0
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and SemanticsCode0
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI ReconstructionCode0
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-ClassificationCode0
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
Implicit 3D Orientation Learning for 6D Object Detection from RGB ImagesCode0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
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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