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 4101–4125 of 7282 papers

TitleStatusHype
Graphs as Tools to Improve Deep Learning Methods—0
Graph Signal Restoration Using Nested Deep Algorithm Unrolling—0
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks—0
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising—0
Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising—0
Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection—0
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
Grayscale to Hyperspectral at Any Resolution Using a Phase-Only Lens—0
Temporal Residual Guided Diffusion Framework for Event-Driven Video Reconstruction—0
Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos—0
Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts—0
Group Sparse Coding for Image Denoising—0
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization—0
Group Sparsity Residual Constraint for Image Denoising—0
Group Sparsity Residual with Non-Local Samples for Image Denoising—0
GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction—0
Temporal Segment Transformer for Action Segmentation—0
VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion—0
GSNs : Generative Stochastic Networks—0
Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression—0
Tensor Based Second Order Variational Model for Image Reconstruction—0
Guidance Free Image Editing via Explicit Conditioning—0
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression—0
Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers—0
Tensor denoising and completion based on ordinal observations—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